{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.537558Z",
     "start_time": "2025-02-06T14:04:28.531690Z"
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    "execution": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=14, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cu124\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29f1d6fe09a9955",
   "metadata": {},
   "source": [
    "# 数据加载\n",
    "\n",
    "- 采用WMT16的德语和英语平行语料库，数据集主页：[WMT16](https://www.statmt.org/wmt16/multimodal-task.html#task1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d047d1eed0336fbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.942222Z",
     "start_time": "2025-02-06T14:04:28.938639Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:21.100821Z",
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     "iopub.status.idle": "2025-02-07T09:47:21.103659Z",
     "shell.execute_reply": "2025-02-07T09:47:21.103102Z",
     "shell.execute_reply.started": "2025-02-07T09:47:21.100798Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # 调用脚本文件\n",
    "# !pip install sacremoses\n",
    "# !sh data_multi30k.sh wmt16 wmt16_cut de en"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a930b5668e422750",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0f0f4781f5e042f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.020273Z",
     "start_time": "2025-02-06T14:04:29.011246Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:21.104856Z",
     "iopub.status.busy": "2025-02-07T09:47:21.104433Z",
     "iopub.status.idle": "2025-02-07T09:47:21.112802Z",
     "shell.execute_reply": "2025-02-07T09:47:21.112111Z",
     "shell.execute_reply.started": "2025-02-07T09:47:21.104836Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class LangPairDataset(Dataset):\n",
    "    def __init__(self, mode=\"train\", max_length=128,\n",
    "                 overwrite_cache=False, data_dir=\"wmt16\"):\n",
    "        self.data_dir = Path(data_dir)\n",
    "        cache_path = self.data_dir / \".cache\" / f\"de2en_{mode}_{max_length}.npy\"\n",
    "\n",
    "        if overwrite_cache or not cache_path.exists():\n",
    "            # parents=True：如果父目录不存在，会自动创建所有必要的父目录。\n",
    "            # exist_ok=True：如果目录已经存在，不会抛出错误。\n",
    "            cache_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "            # 读取源语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_src.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.src = f.readlines()\n",
    "            # 读取目标语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_trg.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.trg = f.readlines()\n",
    "\n",
    "            filtered_src = []\n",
    "            filtered_trg = []\n",
    "            # max length filter,超出最大长度的句子舍弃\n",
    "            for src, trg in zip(self.src, self.trg):\n",
    "                # 过滤长度超过最大长度的句子\n",
    "                if len(src) <= max_length and len(trg) <= max_length:\n",
    "                    filtered_src.append(src.strip())  # 去掉句子前后的空格\n",
    "                    filtered_trg.append(trg.strip())\n",
    "            filtered_src = np.array(filtered_src)\n",
    "            filtered_trg = np.array(filtered_trg)\n",
    "            #allow_pickle=True允许保存对象数组，将过滤后的数据保存为 NumPy 数组，存储在缓存文件中\n",
    "            np.save(\n",
    "                cache_path,\n",
    "                {\"src\": filtered_src, \"trg\": filtered_trg},\n",
    "                allow_pickle=True\n",
    "            )\n",
    "            print(f\"save cache to {cache_path}\")\n",
    "\n",
    "        else:\n",
    "            # allow_pickle=True允许保存对象数组\n",
    "            cache_dict = np.load(cache_path, allow_pickle=True).item()\n",
    "            print(f\"load {mode} dataset from {cache_path}\")\n",
    "            filtered_src = cache_dict[\"src\"]\n",
    "            filtered_trg = cache_dict[\"trg\"]\n",
    "\n",
    "        self.src = filtered_src\n",
    "        self.trg = filtered_trg\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.src[index], self.trg[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.src)\n",
    "\n",
    "# train_ds = LangPairDataset(\"train\")\n",
    "# val_ds = LangPairDataset(\"val\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2a0f23be5871b84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.097548Z",
     "start_time": "2025-02-06T14:04:29.094294Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:21.113952Z",
     "iopub.status.busy": "2025-02-07T09:47:21.113560Z",
     "iopub.status.idle": "2025-02-07T09:47:21.116262Z",
     "shell.execute_reply": "2025-02-07T09:47:21.115737Z",
     "shell.execute_reply.started": "2025-02-07T09:47:21.113932Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(len(train_ds))\n",
    "# print(len(val_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaf27846b953da4",
   "metadata": {},
   "source": [
    "## Tokenizer\n",
    "\n",
    "这里有两种处理方式，分别对应着 encoder 和 decoder 的 word embedding 是否共享，这里实现共享的方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df6173e2f201c301",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.169362Z",
     "start_time": "2025-02-06T14:04:29.138854Z"
    },
    "execution": {
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     "iopub.status.busy": "2025-02-07T09:47:21.117007Z",
     "iopub.status.idle": "2025-02-07T09:47:21.141616Z",
     "shell.execute_reply": "2025-02-07T09:47:21.141129Z",
     "shell.execute_reply.started": "2025-02-07T09:47:21.117374Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18107/18107 [00:00<00:00, 1164534.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab_size: 18111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word2idx = {\n",
    "    \"[PAD]\": 0,  # 填充 token\n",
    "    \"[BOS]\": 1,  # begin of sentence\n",
    "    \"[UNK]\": 2,  # 未知 token\n",
    "    \"[EOS]\": 3,  # end of sentence\n",
    "}\n",
    "\n",
    "idx2word = {value: key for key, value in word2idx.items()}\n",
    "index = len(idx2word)\n",
    "threshold = 1  # 出现次数低于此的token舍弃\n",
    "\n",
    "with open(\"wmt16/vocab\", \"r\", encoding=\"utf8\") as fd:\n",
    "    for line in tqdm(fd.readlines()):\n",
    "        token, counts = line.strip().split()\n",
    "        if int(counts) >= threshold:\n",
    "            word2idx[token] = index\n",
    "            idx2word[index] = token\n",
    "            index += 1\n",
    "\n",
    "vocab_size = len(word2idx)\n",
    "print(\"vocab_size: {}\".format(vocab_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "461056213fba3ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.226412Z",
     "start_time": "2025-02-06T14:04:29.217164Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:21.142686Z",
     "iopub.status.busy": "2025-02-07T09:47:21.142289Z",
     "iopub.status.idle": "2025-02-07T09:47:21.150638Z",
     "shell.execute_reply": "2025-02-07T09:47:21.150106Z",
     "shell.execute_reply.started": "2025-02-07T09:47:21.142667Z"
    }
   },
   "outputs": [],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=128,\n",
    "                 pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx\n",
    "        self.idx2word = idx2word\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.unk_idx = unk_idx\n",
    "\n",
    "    def encode(self, text_list, padding_first=False, add_bos=True, add_eos=True,\n",
    "               return_mask=False):\n",
    "        # 如果padding_first == True，则padding加载前面，否则加载后面\n",
    "        # return_mask: 是否返回mask(掩码），mask用于指示哪些是padding的，哪些是真实的token \n",
    "        # 若启动bos_eos，则在句首和句尾分别添加bos和eos，则 add_bos=True, add_eos=True即都为1\n",
    "        max_length = min(self.max_length, add_bos + add_eos + max([len(text) for text in text_list]))\n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            # 如果词表中没有这个词，就用unk_idx代替，indices是一个list,里面是每个词的index,也就是一个样本的index\n",
    "            indices = [self.word2idx.get(word, self.unk_idx) for word in text[:max_length - add_eos - add_bos]]\n",
    "            if add_bos:\n",
    "                indices = [self.bos_idx] + indices\n",
    "            if add_eos:\n",
    "                indices = indices + [self.eos_idx]\n",
    "            if padding_first:  # padding加载前面，超参可以调\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:  # padding加载后面\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        input_ids = torch.tensor(indices_list)  # 转换为tensor\n",
    "        # mask是一个和input_ids一样大小的tensor，0代表token，1代表padding，mask用于去除padding的影响\n",
    "        masks = (input_ids == self.pad_idx).to(dtype=torch.float64)\n",
    "        return input_ids if not return_mask else (input_ids, masks)\n",
    "\n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                # 如果词表中没有这个词，就用unk_idx代替\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":  # 如果到达eos，就结束\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":  # 如果到达pad，就结束\n",
    "                    break\n",
    "                text.append(word)  # 单词添加到列表中\n",
    "            # 把列表中的单词拼接，变为一个句子\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "\n",
    "\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word)\n",
    "\n",
    "# tokenizer.encode([[\"hello\"], [\"hello\", \"world\"]], add_bos=True, add_eos=False)\n",
    "# raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "# indices = tokenizer.encode(raw_text, padding_first=False, add_bos=True, add_eos=True)\n",
    "# decode_text = tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "# print(\"raw text\")\n",
    "# for raw in raw_text:\n",
    "#     print(raw)\n",
    "# print(\"indices\")\n",
    "# for index in indices:\n",
    "#     print(index)\n",
    "# print(\"decode text\")\n",
    "# for decode in decode_text:\n",
    "#     print(decode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "628dded826e86608",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.254457Z",
     "start_time": "2025-02-06T14:04:29.250423Z"
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    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:21.152594Z",
     "iopub.status.busy": "2025-02-07T09:47:21.152346Z",
     "iopub.status.idle": "2025-02-07T09:47:21.154951Z",
     "shell.execute_reply": "2025-02-07T09:47:21.154427Z",
     "shell.execute_reply.started": "2025-02-07T09:47:21.152577Z"
    }
   },
   "outputs": [],
   "source": [
    "# for i,j in train_ds:\n",
    "#     print(len(i))\n",
    "#     print(len(j))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43df9ce1d4860bb",
   "metadata": {},
   "source": [
    "## Transformer Batch Sampler\n",
    "\n",
    "> Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens\n",
    "句子按照序列长度差不多的分到一个批次。 每个训练批次包含一组句子对，其中包含大约 25000 个源标记和 25000 个目标标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c072c0e714bd8ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.298858Z",
     "start_time": "2025-02-06T14:04:29.288864Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:21.155822Z",
     "iopub.status.busy": "2025-02-07T09:47:21.155604Z",
     "iopub.status.idle": "2025-02-07T09:47:21.161761Z",
     "shell.execute_reply": "2025-02-07T09:47:21.161089Z",
     "shell.execute_reply.started": "2025-02-07T09:47:21.155805Z"
    }
   },
   "outputs": [],
   "source": [
    "# 下面的info对象\n",
    "class SampleInfo:\n",
    "    def __init__(self, i, lens):\n",
    "        \"\"\"\n",
    "        记录文本对的序号和长度信息\n",
    "        输入：\n",
    "            - i (int): 文本对的序号。\n",
    "            - lens (list): 文本对源语言和目标语言的长度\n",
    "        \"\"\"\n",
    "        self.i = i\n",
    "        # 加一是考虑填补在文本前后的特殊词元，lens[0]和lens[1]分别表示源语言和目标语言的长度\n",
    "        self.max_len = max(lens[0], lens[1]) + 1\n",
    "        self.src_len = lens[0] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "        self.trg_len = lens[1] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "\n",
    "\n",
    "# 一个批量生成器，根据词元数目的限制来控制批量的大小。\n",
    "# 它会根据传入的样本信息，在不超过设定大小的情况下，逐步构建批量。\n",
    "class TokenBatchCreator:\n",
    "    def __init__(self, batch_size):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        batch_size (int): 用于限制批量的大小。\n",
    "        功能:\n",
    "        初始化了一个空的批量列表 _batch。\n",
    "        设定了初始的最大长度为 -1。\n",
    "        存储了传入的 batch_size。\n",
    "        \"\"\"\n",
    "        self._batch = []  # 这个就是之前的batch_size，就是一个batch内有多少个样本\n",
    "        self.max_len = -1\n",
    "        self.batch_size = batch_size  # 限制批量的大小,假设是4096\n",
    "\n",
    "    def append(self, info: SampleInfo):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        info (SampleInfo): 文本对的信息。\n",
    "        功能:\n",
    "        接收一个 SampleInfo 对象，并根据其最大长度信息更新当前批量的最大长度。\n",
    "        如果将新的样本加入批量后超过了批量大小限制，它会返回已有的批量并将新的样本加入新的批量。\n",
    "        否则，它会更新最大长度并将样本添加到当前批量中。\n",
    "        \"\"\"\n",
    "        # 更新当前批量的最大长度\n",
    "        cur_len = info.max_len  # 当前样本的长度\n",
    "        max_len = max(self.max_len, cur_len)  # 每来一个样本，更新当前批次的最大长度\n",
    "\n",
    "        # 如果新的样本加入批量后超过大小限制，则将已有的批量返回，新的样本加入新的批量\n",
    "        # 同一批样本计算时，短的样本向最长的样本对齐，所以用max_len来计算\n",
    "        if max_len * (len(self._batch) + 1) > self.batch_size:\n",
    "            # result_batch保存原有的_batch，_batch清空\n",
    "            self._batch, result_batch = [], self._batch\n",
    "            self._batch.append(info)  #箱子里的第一条样本，放入\n",
    "            # 因为是当前batch的第一个样本，所以它的长度就是当前长度\n",
    "            self.max_len = cur_len\n",
    "            return result_batch\n",
    "        else:\n",
    "            self.max_len = max_len\n",
    "            self._batch.append(info)\n",
    "            return None\n",
    "\n",
    "    # 将类的方法转换为属性，从而实现对类属性的访问、修改和删除的控制\n",
    "    @property\n",
    "    def batch(self):\n",
    "        return self._batch"
   ]
  },
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   "id": "c1dd026c49a90114",
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   "source": [
    "from torch.utils.data import BatchSampler\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class TransformerBatchSampler(BatchSampler):\n",
    "    def __init__(self, dataset, batch_size, shuffle_batch=False,\n",
    "                 clip_last_batch=False, seed=0):\n",
    "        \"\"\"\n",
    "        批量采样器\n",
    "        输入:\n",
    "            - dataset: 数据集\n",
    "            - batch_size: 批量大小\n",
    "            - shuffle_batch: 是否对生成的批量进行洗牌\n",
    "            - clip_last_batch: 是否裁剪最后剩下的数据\n",
    "            - seed: 随机数种子\n",
    "        \"\"\"\n",
    "        self._dataset = dataset\n",
    "        self._batch_size = batch_size\n",
    "        self._shuffle_batch = shuffle_batch\n",
    "        self._clip_last_batch = clip_last_batch\n",
    "        self._seed = seed\n",
    "        self._random = np.random\n",
    "        self._random.seed(seed)\n",
    "\n",
    "        self._sample_infos = []\n",
    "        # 根据数据集中的每个样本，创建了对应的 SampleInfo 对象，包含了样本的索引和长度信息\n",
    "        for i, data in enumerate(self._dataset):\n",
    "            lens = [len(data[0]), len(data[1])]  #输入和输出的长度计算放到lens中\n",
    "            self._sample_infos.append(SampleInfo(i, lens))\n",
    "\n",
    "    def __iter__(self):\n",
    "        \"\"\"\n",
    "        对数据集中的样本进行排序，排序规则是先按源语言长度排序，如果相同则按目标语言长度排序。\n",
    "        使用 TokenBatchCreator 逐步组装批量数据，当满足批量大小时返回一个批量的样本信息。\n",
    "        如果不裁剪最后一个批次的数据且存在剩余样本，则将这些样本组成最后一个批次。\n",
    "        如果需要对批量进行洗牌，则对批次进行洗牌操作。\n",
    "        通过迭代器，抛出每个批量的样本在数据集中的索引。\n",
    "        \"\"\"\n",
    "        # 排序，如果源语言长度相同则按照目标语言的长度排列\n",
    "        infos = sorted(self._sample_infos,\n",
    "                       key=lambda x: (x.src_len, x.trg_len))\n",
    "        # 组装批量，所有的batch都放入batch_infos\n",
    "        batch_infos = []\n",
    "        # 批量生成器\n",
    "        batch_creator = TokenBatchCreator(self._batch_size)\n",
    "        for info in infos:\n",
    "            batch = batch_creator.append(info)\n",
    "            # 存够一个batch的样本信息后，会把这个batch返回，否则返回为None\n",
    "            if batch is not None:\n",
    "                batch_infos.append(batch)\n",
    "\n",
    "        # 是否抛弃最后批量的文本对\n",
    "        # 如果最后一个batch的样本数目不足一个batch，则将其剩余的样本作为最后一个batch\n",
    "        if not self._clip_last_batch and len(batch_creator.batch) != 0:\n",
    "            batch_infos.append(batch_creator.batch)  # 最后一个batch\n",
    "\n",
    "        # 打乱batch\n",
    "        # 这里的shuffle是对batch_infos进行shuffle，而不是对batch_infos中的样本进行shuffle\n",
    "        if self._shuffle_batch:\n",
    "            self._random.shuffle(batch_infos)\n",
    "\n",
    "        self.batch_number = len(batch_infos)\n",
    "\n",
    "        # 抛出一个批量的文本对在数据集中的序号\n",
    "        for batch in batch_infos:\n",
    "            # info.i 是文本对的索引\n",
    "            batch_indices = [info.i for info in batch]\n",
    "            yield batch_indices\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        返回批量的数量\n",
    "        \"\"\"\n",
    "        if hasattr(self, \"batch_number\"):\n",
    "            return self.batch_number\n",
    "        # 计算批量的数量,没有用到下面的情况，不用看\n",
    "        batch_number = (len(self._dataset) +\n",
    "                        self._batch_size) // self._batch_size\n",
    "        return batch_number\n"
   ]
  },
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   "id": "71ae51fb9433db98",
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   "outputs": [],
   "source": [
    "# sampler = TransformerBatchSampler(train_ds, batch_size=4096, shuffle_batch=True)\n",
    "# \n",
    "# #为什么这里每个批量的样本对数目不一样呢？长度*batch_number>4096的时候，就会返回上一个batch，然后新的样本加入新的batch,具体要看TokenBatchCreator的40行\n",
    "# for idx, batch in enumerate(sampler):\n",
    "#     print(\"第{}批量的数据中含有文本对是：{}，数量为：{}\".format(idx, batch, len(batch)))\n",
    "#     if idx >= 3:\n",
    "#         break\n",
    "# \n",
    "# print(\"-\"*50)\n",
    "# print(len(sampler))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b1add788035b2a",
   "metadata": {},
   "source": [
    "## DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "24e99727ef49605",
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   "source": [
    "def collate_fn(batch, tokenizer):\n",
    "    # 取batch内第0列进行分词，赋给src_words\n",
    "    src_words = [pair[0].split() for pair in batch]\n",
    "    # 取batch内第1列进行分词，赋给trg_words\n",
    "    trg_words = [pair[1].split() for pair in batch]\n",
    "\n",
    "    # paddings 在前在后影响不大，但在后好理解，所以padding_first=False\n",
    "    # [BOS] src [EOS] [PAD] \n",
    "    encoder_inputs, encoder_inputs_mask = tokenizer.encode(\n",
    "        src_words, padding_first=False, add_bos=True, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    # 目标语言 [BOS] trg [PAD]\n",
    "    decoder_inputs = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=True, add_eos=False, return_mask=False\n",
    "    )\n",
    "\n",
    "    # 用于后续计算loss\n",
    "    # trg [EOS] [PAD]\n",
    "    decoder_labels, decoder_labels_mask = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=False, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    return {\n",
    "        \"encoder_inputs\": encoder_inputs.to(device),\n",
    "        \"encoder_inputs_mask\": encoder_inputs_mask.to(device),\n",
    "        \"decoder_inputs\": decoder_inputs.to(device),\n",
    "        \"decoder_labels\": decoder_labels.to(device),\n",
    "        \"decoder_labels_mask\": decoder_labels_mask.to(device),\n",
    "    }  # 当返回的数据较多时，用dict返回比较合理\n"
   ]
  },
  {
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   "execution_count": 12,
   "id": "f53f13a987ea9201",
   "metadata": {
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   "source": [
    "from functools import partial  # 固定collate_fct的tokenizer参数\n",
    "\n",
    "# # 可以调大batch_size,来看最终的bleu，如果GPU内存不够，可以减小batch_size\n",
    "# sampler = TransformerBatchSampler(train_ds, batch_size=256, shuffle_batch=True)\n",
    "# \n",
    "# # batch_sampler 是一个自定义的批次采样器，用于控制如何从数据集中采样批次。与 batch_size 和 shuffle 不同，batch_sampler 可以更灵活地定义批次的组织方式（如按长度分组）。\n",
    "# # partial(collate_fn, tokenizer=tokenizer) 使用 functools.partial 将 tokenizer 参数固定到 collate_fn 中，生成一个新的函数。\n",
    "# sample_dl = DataLoader(train_ds, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "# \n",
    "# for batch in sample_dl:  #外层是拿每个batch\n",
    "#     for key, value in batch.items():  #内层是拿每个batch里面是一个字典\n",
    "#         print(key)\n",
    "#         print(\"-\" * 50)\n",
    "#         print(value)\n",
    "#         print(\"-\" * 50)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4932b257d82059",
   "metadata": {},
   "source": [
    "# 定义模型\n",
    "\n",
    "- Transformer模型由Embedding、Transformer-Block组成\n",
    "- Embedding包括：\n",
    "    - WordEmbedding\n",
    "    - PositionEmbedding\n",
    "- Transformer-Block包括：\n",
    "    - Self-Attention\n",
    "    - Cross-Attention\n",
    "    - MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2988138abac64d",
   "metadata": {},
   "source": [
    "## Embedding"
   ]
  },
  {
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   "id": "847a70137968848a",
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    {
     "data": {
      "image/png": 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LxYJJkybhH//4B9q3b4/WrVvjgQceQGpqKoYNGxbMoRNCCCEkSAR18nL22WfjnXfewZQpUzBz5ky0bt0as2fPxsiRI1Wde+65B8XFxRg/fjzy8/MxYMAALF26FG63O4gjJ4QQQmoHA3YDJ+i5jS699FJceumlfo9bLBbMnDkTM2fOrNV5Jsf9hugoG16454+q7G9LrlN68w0SJLxokbiF9vQVJxAAvPNZutKPjZQ8SQe9R5WOH7hX6d2/iFMq6t8HlW66Ttwxq7RcPtckS56j/2tzienccb+K82VrpbhzCtPkAxx1uEDpAx5JBNSymZzbsUecQJsrYrUTiPvKvV/6B4DmMZLHqTAyAr7wxsq92lMisUYJmovGeUSsIZqEUSlOoIJSyckEAE0d8jG1y22Gxy2uG0e5nNujuY1gyLndTi2XkpbbKMqmWZhOcBu5tRxBXoeeC0hzu/j5LdJzG3m0cVg1t5FHs8o4bPL+AmZnjzm3kZZjSXdD+cl5pLuQbNDdRpo76aS8Sn5yG0G/B/CJfm/8UVWeIn/moZq7ioJIQxprI8dSj98LD6ym3+eatw9dgj55IYQQQkIRo5ZxK4EkOG0sBD23ESGEEEJITeDKCyGEEBIEGPMSOJy8EEIIIUHAY1hNMWk1b1+Hg2lg8LERIYQQQhoUIbPyMmJrJhwRTqzMnK3Kbhk0Wul/DOmkdNRHPyh9/ZgbTf2kvS/OlKVXSY6gr450VXpmu3eVXhj1B6UPJDWVjn6WHETz956n9NxWS5Se01VyDQFAwrp8pVcUS26k4taaI0pztWyukJxH5zTZrvQPeTLu70tbKl2ZIOdzHJR8RADQIVJ2Of42qr3SRw1xJYXFyL3ZVyR9RVrkY+Yokvj4Q5otyKgQV09RiW4XApo6HUrbj2pOHbc29y6T/EQeuTwTYQ65TxbNbRRulWswHGa3kUPPBWT3nXPHa24ifdl95zCy2rQ+tfp63iHAvCTs8JfDyE+5yyLXqjuE9JxHVS05e2v4bbCmOY90quNOqhI/3z4DCmb01yaEv+GS04cXFnhrsYbgDeEPZshMXgghhJD6BGNeAoePjQghhBDSoODKCyGEEBIEah+wy8dGhBBCCDmDHIt5CfzRT23aNnT42IgQQgghDYqQWXkpeKY57A43CubIJXt+2670G69foHQLxwalLW80MfVj+/pbpSd/d43SZQckH8+Dl/+kdFTKZ0rfefYEpV0frFU653vJl9S0jbh0DomBCQCQsERyJn24r5uMt9UBGV+ktF9ztK3S50RuUfrHg4ly7oJWSpcmissncluu6dzt3OI2WhvbU+kjXnG1JEUfUXrnPsmf5NLcRvZicRUd9EqOJMMjLqTyErNdyOLQ3Ubirql0aXPvCs1d4/a9lBruEEcSTG4jKT/ZbaQ5hhy6a6cauY38fDXQ3UYezS3gPCG3kZ7zRM9t5PWT26hCS6Zks+j5k7TcRpo7qVKzSdlgdjrp7iGrxXe5jj9nj7/yqrZEr2lfNSZ0V9pJPcNby9xGoew24soLIYQQEgSOx7zU5hUIc+fORVpaGtxuN9LT07F27Vq/dc8//3xYLJaTXkOHDlV1brzxxpOODxkyJKCxVZeQWXkhhBBC6hNeWM/4Pi9vvPEGsrKyMG/ePKSnp2P27NkYPHgwNm3ahKZNm55U/+2330Z5uaxOHzx4ED169MDVV19tqjdkyBAsXLhQ/exymffrqmu48kIIIYSECE8++STGjRuHMWPGoEuXLpg3bx7Cw8OxYMECn/Xj4+ORnJysXsuWLUN4ePhJkxeXy2WqFxcXd1qvg5MXQgghJAh4DEutXwBQWFhoepWVlfk8X3l5OXJycpCZmanKrFYrMjMzkZ2dXa0xz58/HyNGjEBERISpfOXKlWjatCk6duyIW265BQcPHgzwrlSPkHls5P4oB3aLA8MGTVJlqcPkeMuFm5U+MKyL0glLfjZ3FCFb7se8J8Gx+rb1Wy8pUrqPU+rszZDb3XallCesk8DJw1eUSHkXCcQFAE9+vtKbt8oW/X/uKx+6b5OkPPuwBBFf0fJHpb0FElj784E0pW1NZRwRR+QaAKCNY5/SZQlupQ95JYiyRaSMb/uWZKUdesBukSw/5lbEygm0tAYoPmG/fZcE8OoBu6UJ0q9RrgXsOs3Bp8eJ1AJ2S+3S1m2VtobdPJ93aMuyenoAm0VLD+Dvt8iUHkDGZLPqW/pr1S3+g2b1wNxyPTDXtN2/HsgrgdEVfurrNkvbCcvP/iyY+phsWh2/6QH8Bt/6LK41+vtyJrA0oHjJgMbagK6vIeKpZcDu8YD/Fi1amMqnTZuG6dOnn1T/wIED8Hg8SEpKMpUnJSXhl19+OeX51q5di59++gnz5883lQ8ZMgRXXXUVWrduja1bt+Jvf/sbLr74YmRnZ8Nm85M/pZaEzOSFEEIIaYzs2rUL0dHR6ufTFW8yf/58dOvWDf369TOVjxgxQulu3bqhe/fuaNu2LVauXIlBgwadlrHwsREhhBASBLyGtdYvAIiOjja9/E1emjRpApvNhry8PFN5Xl4ekpOTfbY5TnFxMRYvXoyxY8ee8rratGmDJk2aYMuWLaesGyicvBBCCCFB4Phjo9q8aoLT6USfPn2wfPlyVeb1erF8+XJkZGRU2fbNN99EWVkZbrjhhlOe5/fff8fBgweRkpJSo/HVBE5eCCGEkBAhKysLL730El599VVs3LgRt9xyC4qLizFmzBgAwKhRozBlypST2s2fPx/Dhg1DQkKCqbyoqAh33303Vq9eje3bt2P58uW44oor0K5dOwwePPi0XQdjXgghhJAg4AWUYyjQ9jXl2muvxf79+zF16lTk5uaiZ8+eWLp0qQri3blzJ6xW87rGpk2b8OWXX+LTTz89qT+bzYYffvgBr776KvLz85GamoqLLroIDz744Gnd6yVkJi/lmb3hdbjR6Z+/q7LCl8TFYnxyVGnbiP1S/qY4UQCgaIhsy5/wyVY5oL3ZM3dfovSjzT9SuuU5u6TfDrItf5PvCpT+slTSEfyp5Xemc39mi1E64lfZMv+88yVK/KuWkmrgt1xxNDVvLR8ib1mp0vm5EuQVIVkD4D0q9wMAmtmLlS6Ll4/N7kppnxYu1rjsQt+LetYScfzkaW11bCUntNV+AexHxXVT6ZZ7gEpx1xgu37/S4XbdbSTvfYRVbIWG3RwZ77RojhoHfOI3PYBN36JftF1LD6CP9MT0ALqryKE5lLyGP1eRDETf7t+f46eqP5pmV5Hhs1xH37rf6sep5K/85M6qV83XuWvTzxmhqjH5O1Yfr4PUCbXfpC6wthMnTsTEiRN9Hlu5cuVJZR07doThxyIYFhaGTz75JKBx1AY+NiKEEEJIgyJkVl4IIYSQ+kRt8hMdbx+qcPJCCCGEBAEvLH43hKxu+1CFkxdCCCEkCHDlJXBC98oJIYQQ0iAJmZUX+x15sEe4YFwleX3eO2up0hdef6fSb5z1uNKjLpNyANh7sbg7wt/Rcg9puXm+/p9s9rPw4lyl70wTm9n9fWSXwqb//kHpxftk2+XpzT4wnfvzJlcqHferOFN6ugqVLmwtLpqK38Ue47L4tsq498pHoDRRoskNj9n5Em+VekcTZM67q0I8/61d4tJyHNFcOrqnpkRcTHtK9ayj4gSyl5iXQg3NVWQ7Kve/0q3VqZByq1Mbu5brJsouLqtDdslR5bbIub3OE3Ib6W4ju+6o0XIb+UndYbHrriLfbiOPtux7Um4j7Rx2LbeRvjGV1aI7mvTcS3oOI60+fLuWrCckvqlOrqLqfPPxl8PIr0Ooys5O/xL56cq5RIgvap/bKHTXH0Jm8kIIIYTUJ7yGxe8Xheq2D1VCd9pGCCGEkAYJV14IIYSQIOCt5WOj2mxw19Dh5IUQQggJAnpm6EDbhyqhe+WEEEIIaZCEzMrLOx0/RnSUFd1um6DK9njEbXTWmA1Kp9rE3VI4QtxJADC182dKv9ErU2nrQXH8tFguzpcFzfsr/fMff1b61t7iHkl4qUjp7F/7Kt02TRwxAFDZWtKLR26RfEgJ1ggZb5oEcIXvEl1mSI4ma1iY0mF50n9FW3HjWGxmC02kVfILlcZL+bYySYh0XqTkWHLK7TCdG0flHLmlktvIYjustJZGCQDgDZP3w1Iq99aj5fzS3VF2l2j9OiK13EZwSJ/hutvIcYLbSHcV+cthpJWbnFVWzQmku41MeYqglZsdXroTyaG5jSq0nEdOLbdRgSHvq+4qMruQfOc8ssG/20i/I/5cQtXJeVRdatympg4hrb61ut/fajymMxBIGcLBmo0FDyym3/NA2ocqITN5IYQQQuoTfGwUOKF75YQQQghpkHDlhRBCCAkCHtTu0Y/n1FUaLZy8EEIIIUGAj40CJ2QmLy8VtITbY8f0UYtU2RVf3qr0xvNfVnrC7guUXtRrvqmfs5wSJfro5TFKR2+T4NOEJRKYG9n+LKXLzpPgyrO7bVW6KLGJ1P9J297/QvO8uqC9BPDGv79DaT1ItKK1BMQmfCB9/e4pU9oaK+OO3CvnCGsiUbZ6UC9gDmwsT5DzbS+R9ADXxUp7Z6FERR7xSsCuUSrj2H9UIn9ddglatpeYTg2PWz6mzoKjWrnU0QN2nU65zxarfKuJtMm9gV2CXt1Wqa+nAAAAq/atyOs7wwK8dt8Ro1Y/6QEcNhlruSkFgDk9gB6Yqwfa6snYrFq/+h+y6gTmVpWR1m96AD9t9G31bdX4JmmYAoL9/wHW73/Nt+4P3WBG0jBgYsbACd0rJ4QQQkiDJGRWXgghhJD6hAFLlSug1WkfqnDyQgghhAQBPjYKnNC9ckIIIYQ0SLjyQgghhAQBr2HxGxxf3fahSshMXl5bOBg2lxvZd89WZS++IMeXnyNOnuz/9lB67u1fmPr5tULcLv0GS0qBLze3VTr2NUkpkPy1bOP/VlFLpW9KWaX0Y2f9WemEn8SZ84O2mz0A5LeXD2pMfr7Sv1fKfvpdW+5RunRXstI/lzdV2kgQt1HYXnHgNI+TXAG5UZGmc+uOJm+cjHFXUazS8dp2+M5CqX/IqzlGyqXtoRK556lOsfI4Ssy2Eo9bS1WguZX09AAw5HzhTu3G6ekBNLeR4dAcTNpuCV6neTHSZtHcRuaMCdKX9lvk0cZhsWnpATSrjENLA6D/8XHZxPUEwJRtVk8P4PWTNkBfQrbpDjTNtWS16O6kKtIDaOewaX8fTW30e+PXheSn3Gfp8TZVHKxvNKSxNnIsDfC98NQyq3Rt2jZ0QvfKCSGEENIgCerkZfr06bBYLKZXp06d1PHS0lJMmDABCQkJiIyMxPDhw5GXl1dFj4QQQkjD4Phjo9q8QpWgr7ycddZZ2Lt3r3p9+eWX6tjkyZPx/vvv480338SqVauwZ88eXHXVVUEcLSGEEFI3eGGt9StUCXrMi91uR3Jy8knlBQUFmD9/PhYtWoSBAwcCABYuXIjOnTtj9erVOOecc870UAkhhBBSDwj6tG3z5s1ITU1FmzZtMHLkSOzcuRMAkJOTg4qKCmRmZqq6nTp1QsuWLZGdne23v7KyMhQWFppehBBCSH3DY1hq/QpVgrrykp6ejldeeQUdO3bE3r17MWPGDPzhD3/ATz/9hNzcXDidTsTGxpraJCUlITc312+fs2bNwowZM04qT56/HnaLE+cOGqXKEr/4TukJH4xRutMbu5V+YEQfUz85h1oo/X8dFiv9RnRXpT/pcrbSxgbJYfT0r5IzaXWffyl9V29J0tP8bTn3m/n9TOc2OoqryKK5aNaVycrVwCablP40V/r96kh7pcuTxEnk2n5I6bMixam0N76v6dxFXnH5xMbLOHILJKdTlFUcQ84j4oLZ74mQa6gUt1FRkYzP4pQ8TCe5jcI0m4/mVvK4fdsLIjS3kcUuH/Fwq+ZCcmi5jXTHjuPE3EYyv/eX28iw6fmCRNtMuY0Em1XLU6S5dOwWcy6rCs3GpLuKyg3f12RyFcGfq0jPeeT/u4u/Z+n+3EPV6Ud3JwWEHzdJTceEquo3QMcKabjQKh04QZ28XHzxxUp3794d6enpaNWqFf7zn/8g7ITEgNVlypQpyMrKUj8XFhaiRYsWVbQghBBCzjxGLbNKG9xht34QGxuLDh06YMuWLUhOTkZ5eTnytf1MACAvL89njMxxXC4XoqOjTS9CCCGENB7q1eSlqKgIW7duRUpKCvr06QOHw4Hly5er45s2bcLOnTuRkZERxFESQgghtccDS61foUpQHxvddddduOyyy9CqVSvs2bMH06ZNg81mw3XXXYeYmBiMHTsWWVlZiI+PR3R0NG677TZkZGTQaUQIIaTB4zVqF7fiDeEYraBOXn7//Xdcd911OHjwIBITEzFgwACsXr0aiYmJAICnnnoKVqsVw4cPR1lZGQYPHoznnnsumEMmhBBCSJAJ6uRl8eLFVR53u92YO3cu5s6dW+tzWVs1h9XmQpPHtEDgDMlh1PGlw0pX7til9HtL+pv6cWjO67i7JLnO2JhflH5loAQiJ/0ibqOK7HgZTx+ZbRf3lnxJnjni+Plg61mmc/dvtU3pfU0Tlf6sQN7GMU1kk79P9jdXeu2BVkofTRFnjzNHrruLW5xOHyf80XTuQ17Ju9M6VhxK322WfE1hFrkfjkJxweRWSi4lw6M5e4o1+45ba1use3OA8kjt6abmNvL6cxs55NweLYdRlFXus9cp5S49388JjiKrtixr+PltMey620jGbnYVaXmfbHIPdIeQw2K+bt0Gacpt5Nc9pDt7Tu0q0utbLf6P2aqxNF1Tx09V9avTl81SjSfedfittCHlzQlorA3o+hoT3loG7NambUMndK+cEEIICSJeWGr9CoS5c+ciLS0Nbrcb6enpWLt2rd+6r7zyyklpfNxut6mOYRiYOnUqUlJSEBYWhszMTGzevDmgsVUXTl4IIYSQEOGNN95AVlYWpk2bhnXr1qFHjx4YPHgw9u3b57dNdHS0KY3Pjh07TMcfe+wxzJkzB/PmzcOaNWsQERGBwYMHo7S09LRdBycvhBBCSBAIxg67Tz75JMaNG4cxY8agS5cumDdvHsLDw7FgwQK/bSwWC5KTk9UrKSlJHTMMA7Nnz8b999+PK664At27d8drr72GPXv2YMmSJYHclmrByQshhBASBI7HvNTmVRPKy8uRk5NjSrtjtVqRmZlZZdqdoqIitGrVCi1atMAVV1yBDRs2qGPbtm1Dbm6uqc+YmBikp6dX2Wdt4eSFEEIIacCcmM+vrKzMZ70DBw7A4/GYVk6AqtPudOzYEQsWLMC7776Lf/3rX/B6vejfvz9+//13AFDtatJnXRD0rNJnik2TomENc6P9X75VZZtfkfw97cf8qLQxQFxIrd/Yb+7IkLD8J/8ibqBJ8dK+cmCB0rZPWyud8rU8/8sZL11e0eUHpX+qFFeJsSHKdOpLe6xX+sW0K5X+em+s0o8kf6G0p0hyEG3f00HpyGRZaozR6rS1i4uoNFHcPwCwu1LyE7WNPKD094fbwhfWIrnWXRXxPuvYirScRZrbyF5izvFTkij1jHJxEnndZnfOcaKdcu58LbdRhFV+oQ2HzNudWs4d7wm5jXRXi7/cRtDcRh7t82G3yfgqNDeH06o5rvTcRlb/uY2sFu8pyz3adxGbntsIFp/llYbv+oD/vSdMDiWtX3/lRh26WPy6kBqSU6YhjZWcdryoZW6j//+7dmIKnGnTpmH69Om1GZoiIyPDtDFs//790blzZ7zwwgt48MEH6+QcgRAykxdCCCGkPmHUwjF0vD0A7Nq1y5QKx+Vy+azfpEkT2Gw25OXlmcpPlXZHx+FwoFevXtiyZQsAqHZ5eXlISUkx9dmzZ89qX0tN4WMjQgghJAgczypdmxeAk/L5+Zu8OJ1O9OnTx5R2x+v1Yvny5dVOu+PxePDjjz+qiUrr1q2RnJxs6rOwsBBr1qw5ral8uPJCCCGEhAhZWVkYPXo0+vbti379+mH27NkoLi7GmDFjAACjRo1Cs2bNMGvWLADAzJkzcc4556Bdu3bIz8/H448/jh07duCmm24CcMyJNGnSJPzjH/9A+/bt0bp1azzwwANITU3FsGHDTtt1cPJCCCGEBIFg7LB77bXXYv/+/Zg6dSpyc3PRs2dPLF26VAXc7ty5E1ar9Hv48GGMGzcOubm5iIuLQ58+ffD111+jS5cuqs4999yD4uJijB8/Hvn5+RgwYACWLl160mZ2dUnITF4+PP85REVZMeL6u1XZ2+fPVnrS0NuU3nmJPINsf6v/XQJf/vwCpSMzJUh06lkfKv3EOdcpnbDkZ6UX7D9P6duaynLbfYmXSv2fzAGp57r3Kv1Yh3ClC7ZJyoPIPtqHxZD2zl2yjFiSIlGDRqVst59kk8DYkkTzL8VvFU2V7hQmKQycBVrQprZVvaVItuLfXRan9SRBqY4jWmBnmKQssBVXQqcyTMZuVMoxi0sLcNUCa6PsEpib75B7owfsep36tvwyDs8JAbtW7cmq199viyk9gGiH3Xdgrh6wqwffuqzm69YDcPX0ACVaSgGnVl6plVu196LCq5XrqRCqCBT0d8xf0Ky/wNyAgmxrEcBYHeoyiNj/Sc5QG9Kg0R/9BNo+ECZOnIiJEyf6PLZy5UrTz0899RSeeuqpKvuzWCyYOXMmZs6cGdB4AoExL4QQQghpUITMygshhBBSn6hNfqLj7UMVTl4IIYSQIBCsx0aNAT42IoQQQkiDgisvhBBCSBDgykvghMzk5bDXgQqvFd0nfa/K2mlXX/jXQqUf7LBM6f/re4mpH9uhI0qnvS/ukNlxg5TePGi+0vdmiBsk9lVJG/DZBklNMK/5l0pXtmumdPTGw6ZzJ9silS5oLx/ayO2iywxxD1nDNKfNbumnYIA4oyyawyjSKq6eo2IuAgBsKZW8FRdEiWvKlS91Srzl2g/iNvq9RNxGFptck10yE8AbrrmNSrR+AFS6JTWBUSH33OHWnEfadUQ7tDTsDtnTP9yipRZwak4eVCMFAADNGGRyVsGmb9GvpQewSrlXc5LY/aQH0B1FAFBhchXJtRYY8r7qriI9w6zNop/bd3oA85b+Zgw/x2rqQvJHTesfaxR4fWt1F5lrOq4z8Z9HCP8H1djh5CVw+NiIEEIIIQ2KkFl5IYQQQuoTXHkJHE5eCCGEkCBgoHZ251De15CTF0IIISQIcOUlcBjzQgghhJAGRcisvPz5w1tgDXNj69UvqLILN/5J6Y96ikNId/VMu140AET9FiX1XlqndELzXkofOL9E6SF9flB6R7NUpWNyxF1TcqG4YA6dJc6axMVbTefWnUToUCR9/VvcJ9sqpS9rvLh8onaJWyWm6SGpEy45knRHRlkTc16lX4vEbXRD7BqlXfmycFlkyDmMo+L42VuSqHSEUyxGDrkEeMLE5mM7LPfv2DHRhkccOW6X3A+T28imdeyQj7hbyx2ku42s2rKtV96Wk/A65Fo9Wt4oq11z9ui5jWwy1nLt3ppzG+k5lsxuI4+hj1F3CUm57iqq1HIYmVxFfpalq8xt5KeNnhfIVo3lbrNryfd3JesJ/dQ891DofvskDRuuvAROyExeCCGEkPoEJy+Bw8dGhBBCCGlQcOWFEEIICQJceQkcTl4IIYSQIGAYlsB2m9bahyp8bEQIIYSQBkXIrLx0eHY37FYXJg/oo8pKXpI8QqWPicXhzaIYpbOGfGDq5/Wd/eSHBTL3S/x8j9JPHjhX6dubrlB6fL/JSjfNEdfNZ0cTlD7cVdwj8UWaawbAr1pen3Nb/ab0nu0tlf76aBulvcnxSoftlr46xUuio1/jkpWu0NxCtqaSmwgAthVIX01ayXW78sUhs98jbhejXFxPB4+IgyrSKXYeZ5Hc88pwLY9PWZnp3JWa2wiayyfCpeVAssmYomzidDJcmttIyw/kceo5heTc3ip+IwyHbxuM7jbyaFYZp01zN2nfkFya66lcS5jk0MoBsxMp3Frms9ym5zbSXDdWi3Zvq+FCsp3wBc6UD8miubH8upB81/dnHKq5oyjINLTxNmIsjei98MJSq03qatO2oRMykxdCCCGkPsGYl8DhYyNCCCGENCi48kIIIYQEAQbsBg4nL4QQQkgQ4GOjwAmZyYtRWATDUo7sp89WZbFvfav0xX+6RemygxIh+ttlL5n6SQ+XQNm7LpigtPPDtUr/Jztd6UeGfa/03v7ylK79QzuUfmn3eUqndslT2hZpTk2wrLiL0pfFr5f2e2SL/88Pd1K6pLkWKLtWztc7UvQviVL/sFcCXZsn5JvOvXOfBOxGWrSg23zZon+3RwKdvVpw8dEjbqUtbpfSjiIJNq2IkKBSlJoDdj1hviP0olwyXotD0gtE2STY2HBIv25tK309YNeUHkC6OQkt7tWUBsCmBexWQA/Y1dMDSGO7lh7Aqz25PTE9gCkw16KnJvDdxhRkqwXy6uVW7W9dVX/4/H2j81fur6+a9nPsYM3G5D8quIb1CTnDcOUlcBjzQgghhJAGRcisvBBCCCH1CaOWj41CeeWFkxdCCCEkCBio3Z5HofwElI+NCCGEENKg4MoLIYQQEgS8sMDCHXYDImQmLzv/2hk2lxvNH1qtymytZVv91BfEBWMtF6fMskzzLbpQ26p+x+WiO3/bVOlmn0n59kuPKN01XZxKJfn5Sm/5Xhw/ky78WOmPW2eYzv3hXnH8vN7x30rPO3hY6W9+76x0WAtte/mlcr5urt+VPpoiTqU9HrnWTrHiegKAHRtTlHZYtC3t88Xxs708URpo2/hbCrR7GCY30FEkTpniFLH5GGXatv8AvGFe+CLWKecucmpuI6uUe11S7tYdO5rbyGaRBciq3EZwaA4ezc1jt+npAaS6U3MVVWgOIXN6AHmPTnQbeUxOJGljTg+gpQHwU+7vD5zZnWTxe8zkxvLrHvJZXGv09+ZMUGdbz5+B9fwajzWUnzHUU+g2Chw+NiKEEEJIgyJkVl4IIYSQ+oTXsMDCTeoCgpMXQgghJAgYRi3dRiH8KJCPjQghhBDSoODKCyGEEBIEGLAbOCEzefn7dW8gPMqGFz+7UpVtuUxy/6T9/WuprDkc/vrlKFM/j/d/U+nJf/hE6f8MuFjp6P9tVfrFQ+cqndX8U6VnxUg+o8Qc+QBeduUGpRefJX0CwN6t0Uond5G8R0al5BfybJNrKm4ubb1l4sBpYddcPsnyEdhcnqR014jdpnN/frg3fGEpLFF6e2kTn3UcR+R+GhF6biNx0FSGS74ko9zsNkKY1NPfmxin5DAqcsi90XMbeZ1S3+3HVWRFdd1Gen4h0Q7tflZozhw9t5G/fEQVht1nOQCUeOWe6LmKdFeRFbrTSc9hpLmNTK4izZ3k9b/w6j8nkWjdhVRneYcC6ctvPzWrHxB0/JBawMlL4ITM5IUQQgipTzBgN3DqTczLI488AovFgkmTJqmy0tJSTJgwAQkJCYiMjMTw4cORl5fnvxNCCCGEVMncuXORlpYGt9uN9PR0rF271m/dl156CX/4wx8QFxeHuLg4ZGZmnlT/xhtvhMViMb2GDBlyWq+hXkxevvnmG7zwwgvo3r27qXzy5Ml4//338eabb2LVqlXYs2cPrrrqqiCNkhBCCKk7jruNavOqKW+88QaysrIwbdo0rFu3Dj169MDgwYOxb98+n/VXrlyJ6667Dp9//jmys7PRokULXHTRRdi92xxaMGTIEOzdu1e9/v3vf/vsr64I+uSlqKgII0eOxEsvvYS4uDhVXlBQgPnz5+PJJ5/EwIED0adPHyxcuBBff/01Vq9eXUWPhBBCSP3n2ATEUotXzc/55JNPYty4cRgzZgy6dOmCefPmITw8HAsWLPBZ//XXX8ett96Knj17olOnTnj55Zfh9XqxfPlyUz2Xy4Xk5GT10v8/Px0EffIyYcIEDB06FJmZmabynJwcVFRUmMo7deqEli1bIjs7229/ZWVlKCwsNL0IIYSQxsqJ/+eVlZX5rFdeXo6cnBzT/6tWqxWZmZlV/r+qU1JSgoqKCsTHx5vKV65ciaZNm6Jjx4645ZZbcPDgwcAvqBoENWB38eLFWLduHb755puTjuXm5sLpdCI2NtZUnpSUhNzcXL99zpo1CzNmzDip/KLwg4gOt2L6veJcmdL5v0q/8V/tzTwoE560Reb53X2O4Ur//MeXlX5qkEyBw/+7X+nF6/sq/Y+L1ivt7dRK6fjvJDdRmj1K6YNdzOeO/FV+LjPEYWTV8gVFbZcArsL+4rrRXTpxVqlfLCmL8NNRsSedF/mL6dxu7XN41NB+MYqLldxRkiCns+Ur7dDmj94wLYdUsbiKKsIlx5JRobmLADg0t5HFJk6bWIdc326nOJ2iLHpuIy13kDZX13Mb6ZzoNtJzGEHLYVShleuuIq8pt5GWjwgyDj23kZ6nyGkxX3eBoeWBsujOJT0vk+ZC8vrJeaS5ivRPlO5UOPFbjP8cRjULEAzIDVELB4+1Ot/HAhrTGQiMDOHgy1ClrtxGLVq0MJVPmzYN06dPP6n+gQMH4PF4kJSUZCpPSkrCL7/8clJ9X9x7771ITU01TYCGDBmCq666Cq1bt8bWrVvxt7/9DRdffDGys7Nh0/5m1yVBm7zs2rULd9xxB5YtWwa3211n/U6ZMgVZWVnq58LCwpPeWEIIISTYGKide/542127diE6WraLcLlcvhvUkkceeQSLFy/GypUrTf9vjxgxQulu3bqhe/fuaNu2LVauXIlBgwadlrEE7bFRTk4O9u3bh969e8Nut8Nut2PVqlWYM2cO7HY7kpKSUF5ejnwt+zIA5OXlITk52W+/LpcL0dHRphchhBDSWDnx/zx/k5cmTZrAZrOd5No91f+rAPDEE0/gkUcewaeffnqSueZE2rRpgyZNmmDLli01u5AaELTJy6BBg/Djjz9i/fr16tW3b1+MHDlSaYfDYQoK2rRpE3bu3ImMjIxgDZsQQgipE2oXrFvzR05OpxN9+vQx/b96PPi2qv9XH3vsMTz44INYunQp+vbt67fecX7//XccPHgQKSkpp6wbKEF7bBQVFYWuXbuayiIiIpCQkKDKx44di6ysLMTHxyM6Ohq33XYbMjIycM455wRjyIQQQkjdUVfPjWpAVlYWRo8ejb59+6Jfv36YPXs2iouLMWbMGADAqFGj0KxZM8yaNQsA8Oijj2Lq1KlYtGgR0tLSVMxpZGQkIiMjUVRUhBkzZmD48OFITk7G1q1bcc8996Bdu3YYPHhwLS6uaur1DrtPPfUUrFYrhg8fjrKyMgwePBjPPfdcQH1duP462MJdyD5b7GCRVnlmN/Ovsq1+5OYYpVNnm4OJE+P7KL17gASMXpOxRukf07Rg3K9l+a4gUwJJ9/WR7f2TFn6ntB4MazvL7JSKe1WCWn/VglqtiRIoG/ObBPIm/kkCh23REgisBzWWJksg6M9HZJY8Kk6uBwDch+S35JBXAm29xZIeYKfWPtop5c4j0o8nUiJinb9LsG+lXBoMj3mb/DC3nE8P2I22FUklh3yUI6xS3+PSt+XX0gDIzvsmvA7zXwOPIQGxVodor/ZXw2mX96Jcu7dOq54GQAsctvhOG2A94S+RVztWncBcL/wEIfv5duavPmDeP8JWjTQA5uBf3wu65nQCfk9dBWciaPb0n4JUD0sovBe1DNgNJMj72muvxf79+zF16lTk5uaiZ8+eWLp0qQri3blzJ6xW+R1+/vnnUV5ejj/96U+mfo4HBdtsNvzwww949dVXkZ+fj9TUVFx00UV48MEHT1vsDVDPJi8rV640/ex2uzF37lzMnTs3OAMihBBCGhkTJ07ExIkTfR478f/h7du3V9lXWFgYPvnkkyrrnA7q1eSFEEIICRUC3SVXbx+qcPJCCCGEBAFmlQ6coO+wSwghhBBSE7jyQgghhAQDw1K7nZVDeOUlZCYvTWa7YLe78fkrko/hqyPtlX71wpeUXtDjD0rve12cPAAQ9+lmpe+8VTJcP5/2jtKXXiB++abZh5T+d2EHpQ/31lxBz4lr6dsyic6+rM1PpnP/sFnaf1IkNvPK5rI1ftiOAqXPTtgu/SZKW93RFJYsjp8th+Rak9LMHw33QXHU7KkUl5ZRLtdxsFAcWzFhUsdZKA9mK6KkX+dRcV9VSlNAc/gAQJRbxmvRXEXxdnEbGW5xMbl1N4/TtwvGc0IagON4nSc4fjT7ic2upQfQHjY7TK4iOYeeBqDcsGv1facHCLea85GYHErQrklz3VgtehqAU7uQbNrfOt2FZLOY/whW5UTy1cbfo3e/z+SreFZf46Vwf/VDOB6ANAwY8xI4fGxECCGEkAZFyKy8EEIIIfWKIGxS11jg5IUQQggJAnQbBQ4fGxFCCCGkQcGVF0IIISRYhPCjn9oQMpMXy+oNsFgcuOdfo1WZU0sdNOPOb5Xu1XyZ0n/402RTP0nPSc6fTR+lK93kNknOc2iQuGgSXv9N6Wd/Pl/p87tuUnpfiqQif+uQJN0Z1eQr07l/+F3yE3209yylj7YJUzrug+1KZ0SKM+rr1LOVzvNI7p8OiZL/6PstLZTW8z4BgOuQOGG2V4i7yagUt1FZvrSxhMmYXIXilCmPlMW+iFLp0xNudhjpxLrEjeVxik0oxib5k7wuKQ/X8gB5XLrbSLS/3EaG/US3kfRlt+uOH6nntvt2D7n8uIrcFt/lTos5p1OF7h7ym9tIy7ek5xfSVpMrvVqOpGrkKarqmL88SdXJeeQPm6WaC8B19Ee+oeXMqfF4G9j1hTJ8bBQ4ITN5IYQQQuoVDNgNGMa8EEIIIaRBwZUXQgghJChY/v+rNu1DE05eCCGEkGDAx0YBw8dGhBBCCGlQBLTyUlxcjEceeQTLly/Hvn374PWanSK//fabn5bB48jVfWFzutF6zkZVZtFcDtdfeYnS/2z1ttJpV2819XN0rbh8Wn4geYv+OyZO6azenyn9nlecRPZscQvdNGGV0lO7jlN66dYkpZ9IyTad23PkiNLbt3ZWOixNlg6jC8RC1cVxUOni5uIE2lwhY+0Tu1PpDfvawh+2fHH2bClL8lnHni8uGISL28hRKO6aomQtL1KZuI2MCLPTRifeJec+4JDcT9E2cXV53bqbR+6H7jbSXS1+3UZO82fZo+cwsus5jKSO26bnMNLcRjbdVaTlNrKcuvzYMd+5ivT8SXp5pZ9yfw4hj+ZCsp6w/GxyLpkcSj67qvE3wCpdEvXx22RNr7s+XgOpf3DlJWACmrzcdNNNWLVqFf785z8jJSUFFkvoPncjhBBCAoJZpQMmoMnLxx9/jA8//BDnnntuXY+HEEIIIaRKApq8xMXFIT4+vq7HQgghhIQMhlHFo9hqtg9VAgrYffDBBzF16lSUlJScujIhhBBCTsaog1eIEtDKyz//+U9s3boVSUlJSEtLg8PhMB1ft25dnQyOEEIIIeREApq8DBs2rI6HcfrpesuPcEY6sWtlrCozioqV/v3lDkr/+fpRSi8561+mfs697C6lW93/tdIzNwxVem2/hUp/0DFD6ZSv5Xz9ssTVsq+3WF9sP2n6D+aFMYtdJolRv8pbd+QsyS8EQ/pNsYnj50hzCez6/mgrpXuEi9sobJ90U2ZofQJAYZGSvxSJg8pik3JXvpY7KEpcRfYj4iqqiNDcRuWSY8kWLuez2DTXEoB4p9y3Ay5xbEVZJeeR1yVtHJqryJzbSCvX3EZ6/iKL48TcRvKzy5TDSPp1WjUXEjS3kUWuSXchObQcRiVel8/yY+fQ28i59dxGVi3xjdkhJJhdRfBZ/0T8LUfXOJdKXQYUamPS30v/DqgAzh3CAZAkCDBgN2ACmrxMmzatrsdBCCGEhBQWo3aJQhtaktG6pFY77Obk5GDjxmP7ppx11lno1atXnQyKEEIIafRwn5eACWjysm/fPowYMQIrV65EbGwsACA/Px8XXHABFi9ejMTExLocIyGEEEKIIiC30W233YYjR45gw4YNOHToEA4dOoSffvoJhYWFuP322+t6jIQQQkjj43jMS21eIUpAKy9Lly7FZ599hs6dZYv6Ll26YO7cubjooovqbHB1yexmaxEdZUO7O29WZdFb5Y1PelkcUget8vjLM9O8LjdwsNTb8Xyq0vYVMdLmbGmzP6OJ0omLf1BaDwSt6CNBr03/LUG2v1ZIQCoA2JJkRStuswRwJlwskba2KAlodVjk7S1pLkGpOQUSsHtF1PdKhx2QMR32SpAtABhHZIzbjsg4IpwSdOvMl/qVURKI6txTIOWRcp+8FXIN4WHSjx6YDAAJDmkPl/QbZZUxerT0AA49MFeqm/A65Vo9WpCz1X5C0KwWzOvUjpVr53DbJDBXD7J1Wf2lAfD4rG+zmFMT6IG5pu3+te36TekBvL6/i3j9ZJ7VA11tJ9TxF5hrmIKCrX7Ka5tOoBH/QQ7h/2yID/jYKGACWnnxer0n2aMBwOFwnJTniBBCCCGkLglo8jJw4EDccccd2LNnjyrbvXs3Jk+ejEGDBtXZ4AghhJBGCzepC5iAJi/PPvssCgsLkZaWhrZt26Jt27Zo3bo1CgsL8cwzz9T1GAkhhJDGBycvARNQzEuLFi2wbt06fPbZZ/jll18AAJ07d0ZmZmadDo4QQggh5EQC3ufFYrHgwgsvxIUXXliX4yGEEEJCA+6wGzDVnrzMmTMH48ePh9vtxpw5c6qsWx/t0vfu7QPnEQf+faU81lp4YIDSO5ZIluwm72xUeszoq039vNbuLaWHXCypAlJWHFB6/s2dlD7YX1w08S+LYye7TPanv7rDd0qv2yht3z3Sw3TuijZJSkdsPqx0etJmpdckiwPsqCFunLDmR5TeeKCpjLuVfATC9ok7Zlel2abjPSrOp7zD0Uq3DctX2p2vOaiipV/nFmlbEal1qrl8YsKljsVh/ljG2+W+GW4JFI/Qtt+v1NIAOCzi0vGcHFcOwOw20p1fdueJW/TLMbdddxXJE1fdVVSuu4pMbiMZU7jmkjKlAMCJ59a29de20vTnQqrU6tu0v2l6GgCbRUvhUIWrx/DTxq95qIauohqnGQD8/6Gu6dJ5CC+110dCeZdY7rAbONWevDz11FMYOXIk3G43nnrqKb/1LBZLvZy8EEIIIaRxUO2A3W3btiEhIUFpf6/ffvvttA2WEEIIaTQEKWB37ty5SEtLg9vtRnp6OtauXVtl/TfffBOdOnWC2+1Gt27d8NFHH5kvwzAwdepUpKSkICwsDJmZmdi8ebOf3uqGgNxGM2fORElJyUnlR48excyZM2s9KEIIIYTUPW+88QaysrIwbdo0rFu3Dj169MDgwYOxb98+n/W//vprXHfddRg7diy+++47DBs2DMOGDcNPP/2k6jz22GOYM2cO5s2bhzVr1iAiIgKDBw9GaWnpabuOgCYvM2bMQFFR0UnlJSUlmDFjRq0HRQghhDR2LJC4l4BeAZzzySefxLhx4zBmzBh06dIF8+bNQ3h4OBYsWOCz/tNPP40hQ4bg7rvvRufOnfHggw+id+/eePbZZwEcW3WZPXs27r//flxxxRXo3r07XnvtNezZswdLliwJ+N6cioAmL4ZhwGI5+bZ9//33iI+P99GCEEIIIaeDwsJC06usrMxnvfLycuTk5Ji2NbFarcjMzER2drbPNtnZ2SdtgzJ48GBVf9u2bcjNzTXViYmJQXp6ut8+64IaWaXj4uJgsVhgsVjQoUMH0wTG4/GgqKgIN998cxU9BI9v5/WEzenGlIc+V2XPNpMb23XsBKVbPS7un9z/dDH14/qbOD0qLhPHj2f+FqWf/nag0n/qJbmQNqRJTqH5ebFK/z1Vnh9+u0PsOG/tkBxLAGBp71a6yXo53wWR4o5a1aq/0r9XijumR7Lshpz9Y3ulI63Sp3u/OH5+KU8xndvwaPl4DkobS3i40q4CqVMaq7lrNKdSZaTZUXOcBLc8hix3mZ1O8fZipb1hYh8K13IBVYbJPNzkNpKhmjAcuttI+rGfkNuoXHcb2Xy7h8Js5T7L3RapX2rIuGMtcq0VXv+5jbyae8ipOZEqTS4kvb6e80j73fST86gqx4/He+rcRtXtS43JUs3vSnXkoGhoTowaj7eBXR/xQR1ZpVu0aGEqnjZtGqZPn35S9QMHDsDj8SApKclUnpSUpPZsO5Hc3Fyf9XNzc9Xx42X+6pwOajR5mT17NgzDwF/+8hfMmDEDMTGSZM/pdCItLQ0ZGRl1PkhCCCGk0VFHiRl37dqF6GjZwsLl8pORthFRo8nL6NGjAQCtW7dG//79fSZnJIQQQsiZIzo62jR58UeTJk1gs9mQl5dnKs/Ly0NycrLPNsnJyVXWP/5vXl4eUlJSTHV69uxZk8uoEdWOeSksLFS6V69eOHr06EnP2Y6/CCGEEHIKzrBV2ul0ok+fPli+fLkq83q9WL58ud+nJhkZGab6ALBs2TJVv3Xr1khOTjbVKSwsxJo1a07rk5hqr7zExcVh7969aNq0KWJjY30G7B4P5PV4fMc1EEIIIeQYwdhhNysrC6NHj0bfvn3Rr18/zJ49G8XFxRgzZgwAYNSoUWjWrBlmzZoFALjjjjvwxz/+Ef/85z8xdOhQLF68GN9++y1efPHFY2OwWDBp0iT84x//QPv27dG6dWs88MADSE1NxbBhwwK/uFNQ7cnLihUrlJPo888/P0VtQgghhNQ3rr32Wuzfvx9Tp05Fbm4uevbsiaVLl6qA2507d8JqlYcy/fv3x6JFi3D//ffjb3/7G9q3b48lS5aga9euqs4999yD4uJijB8/Hvn5+RgwYACWLl0Kt9uPY6IOqPbk5Y9//KNP3VCIXvwt7BYH+g+S1AWP939T6b9e+7HSb/10kdIp/xVXDwA8PL6f0s90W6z0rOjzlE5cIXmLbr3gC6VHpksupI0/SGR2p1arlNZzCOVvSDCd29JBVrvijkiuoi5OceMUpsm515c1U/rcWLmOdbmS/0jHelj27tlQ0sxnHQBwHhKHjBETIeWHxXVT2FJcSMZRbaOiSHHgQHOfJLrl3Ltd5me3sTa5Po9bPrJu3e2mxadZtaehXrkdZlzi7PFojiKn44T8Qpprx23XcxjJPdBzG5UaTq1c3F4l2kAcmgupTHMhVTe3kdlVpOc8OrWryGpyIfkuP7GN+YDvYn+OiRr3UwV+8yfVJTXM0UTHD6kVdRSwW1MmTpyIiRMn+jy2cuXKk8quvvpqXH311SdX/v9YLBbMnDnzjG5SG9A+L0uXLsWXX36pfp47dy569uyJ66+/HocPH66ipZnnn38e3bt3V8FGGRkZ+PhjmUSUlpZiwoQJSEhIQGRkJIYPH35S4BAhhBDSIAlSeoDGQECTl7vvvlsF5v7444/IysrCJZdcgm3btiErK6va/TRv3hyPPPIIcnJy8O2332LgwIG44oorsGHDBgDA5MmT8f777+PNN9/EqlWrsGfPHlx11VWBDJkQQgghjYQaWaWPs23bNnTpcmzztv/+97+47LLL8PDDD2PdunW45JJLqt3PZZddZvr5oYcewvPPP4/Vq1ejefPmmD9/PhYtWoSBA49t+rZw4UJ07twZq1evxjnnnBPI0AkhhJB6QTACdhsLAa28OJ1OlZjxs88+w0UXHYsRiY+PD9gq7fF4sHjxYhQXFyMjIwM5OTmoqKgwbTncqVMntGzZssoth8vKymjdJoQQUv85vsNubV4hSkArLwMGDEBWVhbOPfdcrF27Fm+88QYA4Ndff0Xz5s1r1NePP/6IjIwMlJaWIjIyEu+88w66dOmC9evXw+l0IjY21lT/VFsOz5o1y3dyyLO7AHY3Oj0ugaH3jhmp9Jbr5ik973rZvj3iPXMMz3+Wy/b7D1/3g9LTzumgdJOVvyudZo9SOleaIn6d3PrDQ+V8Nm3X4vgN5ksoulwmYlanBIAmWCVo9kia1P/6SDulr49frXT4XqlT6JUAYSO/QOmNheYNiyz2g0q7RMITHSZjL5S+yqO0gN1yCeR1RYq22CTotYlTC9h1J5rOHWuV++MJ0wN2pX2l2/cvsR7Iq6cBsGiBuRVauR6UCwAV2h8Ht00CcPXt/vWAXT09gFMLzC0w5D45LNq5tSDbE9MDVOqpA/wE5urfPsyBuYI3gC39a5oGwG8wbSDfDLU21up8v6rpH/Az8Qc/hP9TITUgSAG7jYGAVl6effZZ2O12vPXWW3j++efRrNkxZ8rHH3+MIUOG1Kivjh07Yv369VizZg1uueUWjB49Gj///HMgwwIATJkyBQUFBeq1a9eugPsihBBCSP0joJWXli1b4oMPPjip/KmnnqpxX06nE+3aHVsh6NOnD7755hs8/fTTuPbaa1FeXo78/HzT6ktV2xgDx3I6hEJeB0IIIQ0bxrwETkCTF+BYjMqSJUuwceOxjMZnnXUWLr/8cti0RwGB4PV6UVZWhj59+sDhcGD58uUYPnw4AGDTpk3YuXMnkz8SQghp+PCxUcAENHnZsmULLrnkEuzevRsdO3YEcCzWpEWLFvjwww/Rtm3bavUzZcoUXHzxxWjZsiWOHDmCRYsWYeXKlfjkk08QExODsWPHIisrC/Hx8YiOjsZtt92GjIwMOo0IIYSQECagycvtt9+Otm3bYvXq1SplwMGDB3HDDTfg9ttvx4cfflitfvbt24dRo0Zh7969iImJQffu3fHJJ5/gwgsvBHDsMZTVasXw4cNRVlaGwYMH47nnngtkyIQQQkj9opaPjbjyUkNWrVplmrgAQEJCAh555BGce+651e5n/vz5VR53u92YO3cu5s6dG8gwTeyd5IEtvBLNr92myjq+KK6PWReJW+hffRcofdfgCaZ+Wr9XpvSyYXL7dmWKbvOJBAlvrhAXzTlnb1L64P+JK+ujYtFGG9mWP/4nSQEAAGfftlnpbU3FkVNhiKulsrU4ftbub6X0/UmSpiByr1z3Lo98+o0i2YZ/68FU07lbRki/7sPSpjxGXE/hu8WGVBEtqQ0MLVFnTLikCrA4xbGT5Dgg9cPMe/pHWcWhVBkmMeYui9xzj58wJ69LxuoxxM1jc2qOH0N3G4mj6NgxOV+Y5jaqgDweDbfJZ6LUK9cUbpXyMq1cdyHp7iTHiW4j7dwmt5FebvFTX0+dYEonIFp3DtlOSLSq/03UHT+GyQlUG0fNmXD8nP5TkOoRyrEZVcLHRgETkNvI5XLhyJEjJ5UXFRXB6fSXTIYQQgghpPYENHm59NJLMX78eKxZswaGYcAwDKxevRo333wzLr/88roeIyGEENL4YG6jgAlo8jJnzhy0a9cO/fv3h9vthtvtxrnnnot27drh6aefrusxEkIIIY2O41bp2rxClRrFvHi9Xjz++ON47733UF5ejmHDhmH06NGwWCzo3Lmz2q+FEEIIIeR0UaPJy0MPPYTp06cjMzMTYWFh+OijjxATE4MFCxacujEhhBBCSB1Qo8nLa6+9hueeew5//etfARxLyjh06FC8/PLLsFoDegJ1xvis9/8hOsqKP4yfrMqSnl+j9OuLBil9722/KL3rOrP7pP1fJOHQpPXXKj3gD1K+L0V2AH52/wVSP2WZ0tM2Sfn8nQOUPnqW5DaK+2Cj6dyXxq1X+p9trld6t0ecQN1a7FH6+y0tlE7oLvmPwnKl/s9lKUp7tRxExQekPgBYIrX2B8WpUxanuW6Kpd+KaLNz5jiJ4eK+8rjFItTELnmbvGFm61CUlguoMkzP36M5atw+TwevU8ZRCenHYcptJGuvYQ7z+11qyK9ImFXLbaS5h9x+3ENOUw4j3VWkXY8pf5H/3EZW3VWk50Py4x7S8VfuL+dRIH3pz95tFt2ddOr6Jx+rWZuGtHQe0Fgb0PWRGkK3UcDUaMaxc+dOXHLJJernzMxMWCwW7Nmzp4pWhBBCCDkRxrwETo0mL5WVlXC7zV9xHQ4HKioq/LQghBBCCKlbavTYyDAM3HjjjabEh6Wlpbj55psRESGPFd5+++26GyEhhBDSWAnh1ZPaUKPJy+jRo08qu+GGG+psMIQQQkjIwJiXgKnR5GXhwoWnaxyEEEIIIdUioNxGDZH/lcYh3GHDyJs/VWXv/S4Oo1av/Kb0jBHdlH65/6umfh5zSe6myPejlL5/5sdKjx5wp5zjO3H8PD30G6U9WnqFXeu7Km05S5wW0f/KN537bNdhpfPbhymdUyZ5iAY2EafUL5/7zu5t2y/Onu9KWvms49pn/mgY0ZFy7KC4ko40D1faW1Ki1RcHDjT3SdMwue5ct9y/BLvmQgo3n9ut5d3R3UYOPbeRH7eR4RIHj0dLzONyyvhKNXdL+Am5jco191CYrVxrI2kwXJoLqcQr5Q5/OYyg51WyavWrmdvI6ztUzaOVm5xYXt8OLa+3ivxC/r7R1cKFVG+o0ukUQBtCAqS2QbehHLAbMpMXQgghpF7Bx0YBU783ZyGEEEIIOQGuvBBCCCFBgI+NAoeTF0IIISQY8LFRwPCxESGEEEIaFCGz8vLAG9fD5nZjw/i5quyt8b2U9n4qDpw33j1P6Rk3Sc4iAJg6qIvSTT7ZpnTbh8WNsydTXCOJX8st3jdEHDW2+Dips06mz0evE0eR1WW20CRYZSPAgg7SZnm+jGl84iqlX/19qNKHvZoT6FC+0t8dbi7nc+5X2n3AdGp44sRVZDssfZXFSLmh5UYKiyqVfh1yD1JdBUrnhiUqnWAtVrryBLdRuOYq0t1GpvG55X54tRxBVpeew0jK9RxGFbrbSHMUAUCp4fB5zJzbSPo67JX3yK25kMq8cg16zqNyrdx2wteoSpN7SNCdPXq5x497qDp5iqwnfI8xH9PzJ/nsqopvgKd2IZ18bn/nqMIdVRf1A+FMnIM0XrjyEjAhM3khhBBC6hOMeQkcTl4IIYSQYMCVl4BhzAshhBBCGhRceSGEEEKCAVdeAiZkJi9p8zbBbnXiivMvUWUfdpet/4dcf5fSbd6QaNWnh6eZ+vn9cgm2bL8kV+mVpRK4NzI9W+l1T3VSemFBT6Uru8i2/HHfHZRxTJEA4TUtpC0AHDXKlHa2lwDjr/e0VvqxFAnYjdolAaNbKiTA1KulJti2T8bRNlICccP3mX8ryhJk2/uIHfuULo9torThkXuTECUBuBantE1x7pX64ZKdPMoqwbCV4eYFQYdFttav9JMGQA/YrTBkHA6XbNFfZkjAbrhD3+pfTwFgTg+gB+zqaQD07f7DrWU+y/U0AHrArlV7UF3plfonBuyW68e0uFBT2gAtdYJXC461mYJsLT7rV7lLfg0Dc/2mB/B7ggBSE9RVfXLaCOUYjEBgzEvg8LERIYQQQhoUnLwQQgghwcCog9dp4tChQxg5ciSio6MRGxuLsWPHoqioqMr6t912Gzp27IiwsDC0bNkSt99+OwoKCkz1LBbLSa/FixfXeHwh89iIEEIIqU/U58dGI0eOxN69e7Fs2TJUVFRgzJgxGD9+PBYtWuSz/p49e7Bnzx488cQT6NKlC3bs2IGbb74Ze/bswVtvvWWqu3DhQgwZMkT9HBsbW+PxcfJCCCGEEMXGjRuxdOlSfPPNN+jbty8A4JlnnsEll1yCJ554AqmpqSe16dq1K/773/+qn9u2bYuHHnoIN9xwAyorK2G3y3QjNjYWycnJtRojHxsRQgghwaCOHhsVFhaaXmVlZagN2dnZiI2NVRMXAMjMzITVasWaNWuq3U9BQQGio6NNExcAmDBhApo0aYJ+/fphwYIFMPw6BPwTMisvljA3LFYXjjwh2+Efmis3rMNfNip94GXZov/Zj2VpCwDuv/Qdpd/sNVDpBza3UXrJWf9SeuRmcdrM/6m/0uF9ZFv9lOd/VvrK6HVKL+8ywHTunyvEoTGo5WalP/iqt9KRZ4sdJ2y3uIq+OSqOJN0VVJkbprQlOlra7je7boqaadvkF2lb+cdWwhcp4XLuojAZU5JDnn96InS3kTiBKiKqcBuFwyeGS9rr6QFcThlfufYLEm6X6ys15Ncgwm7+pTelB7CW+yyPt8pzYD1tgJ4GoFJzITmhl2spAE4w4OjpAXT3kMeUNqA65TVLG1DVMbNzqRrffepwWbvOlsjPgEMjoLGGsHMkZKkjq3SLFi1MxdOmTcP06dMD7jY3NxdNmzY1ldntdsTHxyM3N9dPKzMHDhzAgw8+iPHjx5vKZ86ciYEDByI8PByffvopbr31VhQVFeH222+v0RhDZvJCCCGENEZ27dqFaO3Lp8vl8lnvvvvuw6OPPlplXxs3bqzyeHUoLCzE0KFD0aVLl5MmUQ888IDSvXr1QnFxMR5//HFOXgghhJCGgAV+U5dWuz0AREdHmyYv/rjzzjtx4403VlmnTZs2SE5Oxr59+0zllZWVOHTo0CljVY4cOYIhQ4YgKioK77zzDhwOR5X109PT8eCDD6KsrMzvpMsXnLwQQgghweAM77CbmJiIxMTEU9bLyMhAfn4+cnJy0KdPHwDAihUr4PV6kZ6e7rddYWEhBg8eDJfLhffeew9ut59dRTXWr1+PuLi4Gk1cAE5eCCGEkKBQX63SnTt3xpAhQzBu3DjMmzcPFRUVmDhxIkaMGKGcRrt378agQYPw2muvoV+/figsLMRFF12EkpIS/Otf/1LBw8CxSZPNZsP777+PvLw8nHPOOXC73Vi2bBkefvhh3HXXXVUNxyecvBBCCCHExOuvv46JEydi0KBBsFqtGD58OObMmaOOV1RUYNOmTSgpOZZWZt26dcqJ1K5dO1Nf27ZtQ1paGhwOB+bOnYvJkyfDMAy0a9cOTz75JMaNG1fj8YXM5GXzbS1hdbvRNkvyDl0ycoLSP583X+nLe49Sut0iySEEADdeL7l5nrg0Vmnr51Inuqssf1ns8rwv4usIpQt6i6slqUIcMZ2054MHu5rfno8Luyt9adx3Sq/cJnY2Pf+RJU9yJn2VLx8mi02uKWyvOEa8CVFKOw9IniMAKO0aK/WK5Zg9VjufTRw1zcPFsfVLuOwJ0NQmLqTKSM3JY3IUmZ8CWzVHv0dbhdRdRXCLg6dUy23kdoirqExzykQ6yrT6vh1FgNk95LZIXwWecJ/leg4jh0XLq+Txnduo3KPnQjJ/jTK7h3yX6/hzG/p1DvlxIR07WNNcRacu19/HKp2RdZUniY4fUt+px4kZ4+Pj/W5IBwBpaWkmi/P5559/SsvzkCFDTJvT1YaQmbwQQggh9Q5OmAOCm9QRQgghpEHBlRdCCCEkCNTXgN2GACcvhBBCSDCoxzEv9R0+NiKEEEJIgyJkVl4WXjYPkVFW3LXsVlWW9oK4Ulb2ExvLlhGyU2Gbe8SdBABbK8Rp0+ti2Ub54CTJmfTfv2ibAHWRnEfJX4nLJ+PPW5Te0SzF55hLzzpq+vnjPV2UvqVrjtIx2+U6fqsU7c2XPELf57ZXumWkuHQicmXqXpooeY7CfxZXFQCUJcQqbVSKuyY+Wu6HRdtkqKVL2m+MbCv1bVK/IlKcNuEWcfVU+MlfBACecBmvx5DrsLvF2VOhlUc49XxEcr4Iu+88RZH2UtP5SrxyTeFWcSjtrYhV2qm7ikxuIxmHnsPICd/lthNzG5mOabmK9PxC2v6cXq/v+l6tvj/Hj/WEfT79mgb8ftOrzT6h1SSY3zJr6oBq5ITy44q6hI+NAidkJi+EEEJIvYKPjQKGj40IIYQQ0qDgygshhBASBPjYKHA4eSGEEEKCAR8bBUzITF6SbGWIslnhvmePKvNesFvpv378F6UnDv1E6c8Wytb7AHD7b82UfrndG0rftH6g0jN/GKq0O0O23E9aKFv6j038n9J3dZMg4l8qJBh2YPtfTef+bG1XpRO6S6qBiG2y5f7XJRIc6y2XoNSjv8s4LLESkByxV4JNS5Lk4xCWLX0CQHm8B75oEZ0v7SMk0jbVIekBvJESDB1rlX4qIuWppUNPDyCXdhLeMGlfCdEul1xHqRZtGuWUINtiLTA3wqaVm4JyT0gPoLWJtxZJuSltgJy7XAvYdWrj08utWuxnpR5ke0LQq780AP7L/aQB8JceoKogVO2Pos2iB/nWcCv+ALbub0jfJms81gZ0beQMwMlLwDDmhRBCCCENiqBOXmbNmoWzzz4bUVFRaNq0KYYNG4ZNmzaZ6pSWlmLChAlISEhAZGQkhg8fjry8vCCNmBBCCKkbjse81OYVqgR18rJq1SpMmDABq1evxrJly1BRUYGLLroIxcXFqs7kyZPx/vvv480338SqVauwZ88eXHXVVUEcNSGEEFIHGHXwClGCGvOydOlS08+vvPIKmjZtipycHJx33nkoKCjA/PnzsWjRIgwceCymZOHChejcuTNWr16Nc845JxjDJoQQQkgQqVcxLwUFx3aEjY+PBwDk5OSgoqICmZmZqk6nTp3QsmVLZGdn++yjrKwMhYWFphchhBBS37AYRq1foUq9cRt5vV5MmjQJ5557Lrp2Peaqyc3NhdPpRGxsrKluUlIScnNzffYza9YszJgx46TyIV/cAmuYG1sufFmVXZI+WulOL8hW+llXbVP6pRFDTP04PmyidJPJ4qKBTdwy7hXi7CnIkO3mE58Xh0svp9z6fX3EufJWgbibrk1YYzr32s09lD5qSF/WvfuVXnGos9IWW77S4b/LPNWTGKu0K1ce0R08S8q9xbKNPwA4EuQ6LNq1to44qPSGcHFiJdvlflZEO5WOssh1l0f63ra+8oT0AF5tO324xcFTaoiOcIlLqExzuEQ6yrT6cp+jtTQAegqACC0FAADsr5T30m0RJ5g5DYCWHsDjOz1AuUfumUNb663Qyk/8JqG7iqymNAAWn+V+XUV+XEhVbXnvv42/BiL9pSCo7rn9DyrwMVWrvKpzEHI6oNsoYOrNysuECRPw008/YfHixbXqZ8qUKSgoKFCvXbt21dEICSGEEFIfqBcrLxMnTsQHH3yAL774As2bS4LD5ORklJeXIz8/37T6kpeXh+TkZJ99uVwuuLQEgYQQQkh9hDvsBk5QV14Mw8DEiRPxzjvvYMWKFWjdurXpeJ8+feBwOLB8+XJVtmnTJuzcuRMZGRlneriEEEJI3UG3UcAEdeVlwoQJWLRoEd59911ERUWpOJaYmBiEhYUhJiYGY8eORVZWFuLj4xEdHY3bbrsNGRkZdBoRQgghIUpQJy/PP/88AOD88883lS9cuBA33ngjAOCpp56C1WrF8OHDUVZWhsGDB+O55547wyMlhBBC6hY+NgqcoE5ejGrYvNxuN+bOnYu5c+fW6lwdZhfBbqvAM33bqLJf/youmPZ/+UHplaXiOPjTFZKDCADWjeik9Owbxdnj6dNR6ZQV4v4ZdvMGpde0lbZlxmqlLb3FmfPu9m5K390nx3TuuE3idvmpXHMPHTwk4/td8h+1jRG3S+RuudelKWFKR6z7XemjTWOVNirlXACQFCeWc2uYtE9z7ZAxRXdQOtEmLqbyKHHUuDS3UYWfHEaVEV7TzxWaq8gZrjl+DKkX5RL3ULEh54iy+8lhpOU20vMUhZ/gNioz5TCScx/1SLnJVaS5kGzamm65V+6BTc9tZGi5jSwn5DbSnC82P64ivY3X8O3e0n/NTO4k82024T/vUQ2dS4H8cQ3hP8j1jVD+z/GMQLdRwNSLgF1CCCEk1ODKS+DUG6s0IYQQQkh14MoLIYQQEgz42ChgOHkhhBBCgkQoP/qpDXxsRAghhJAGRcisvBhbd8KwOPDKCxersjcmz1H6notvUfqm7F5KbzxfciEBwKW/iGvkhZUDlXYPkvIWM8W5NDZurdIfZfxR6VWlkjNnZPtvlX7l/UFKR56t5U4CEL5VXEUfH+ku1+YRN46xTSw8lsQE6WuXuHEOdxK3UNjhfKUrm0p+oBNpGy05jPZFRSrdynlA2sfIeOOt8nWiLEbmyA6L3KcKuQUmvOEe08+62yjMLWMs1Ww00U7dVSQusmiH7xxGUVbf5Yl2cyLPEo/05S+3kRMyPt1V5NC+UlVqeYp051ClKbfRCW4jP7mNPH7yDvnLR2R4fX9HMbuWTqgTSF6gGhDQt826zGFUQ2o8Xn6bJtXBMKpIAFbN9iFKyExeCCGEkPoE3UaBw8dGhBBCCGlQcOWFEEIICQZ0GwUMJy+EEEJIELB4j71q0z5U4WMjQgghhDQoQmblJW9ML9hcbiS/sE6Vdb5HXCKH/iq5eJq/LG6aH841T23trVoonfa+HAubsktp4+lopZvZxFKzL0Pqv7RHnEf/bPW20u9tEAfTzsojpnN7d+9VeunuLkrHhO1WOmq71C9LjVHateuw0iXni9vIe1RcNzFNipS2usxOpw4ReXId0ZJ/qZk9X+nyWHHmRFkl9095tO+cO5WRsuZZYVQqbQsXDQBlmpsnyq25igxx6phcRYbmNrIdVfqIV64pyib1d5Q1UdptNed0Oqq5jZwW3VUkvzqm3EYm95BQ4fVdrjuHbCe6jTy+v1t4TS4kPYeRfp/1XEg+uznFkvOpcxX5y59krh9AziO/eZXqiNPdfwMklAM/gwofGwUMV14IIYSQIHDcbVSb1+ni0KFDGDlyJKKjoxEbG4uxY8eiqKioyjbnn38+LBaL6XXzzTeb6uzcuRNDhw5FeHg4mjZtirvvvhuVlZV+evRPyKy8EEIIIfWKerzPy8iRI7F3714sW7YMFRUVGDNmDMaPH49FixZV2W7cuHGYOXOm+jk8PFxpj8eDoUOHIjk5GV9//TX27t2LUaNGweFw4OGHH67R+Dh5IYQQQohi48aNWLp0Kb755hv07dsXAPDMM8/gkksuwRNPPIHU1FS/bcPDw5GcnOzz2Keffoqff/4Zn332GZKSktCzZ088+OCDuPfeezF9+nQ4nU6f7XzBx0aEEEJIEKirx0aFhYWmV1lZWdUnPgXZ2dmIjY1VExcAyMzMhNVqxZo1a6ps+/rrr6NJkybo2rUrpkyZgpKSElO/3bp1Q1JSkiobPHgwCgsLsWHDhhqNMWRWXkaM/QzuSDs++0zejKs3X6X0R31eVPqmayRodlTOGFM/rqESjJu08Dulp8/7n9L3ZEiqgR/Llyqd2fcnpT9b21XptLYS1Bv7U4HSnxR3MJ3bq30I8jZLkGlcU/mgxmyTgNOiFlqw6TrZ3r80OVE6NSTYtF281CmJlqBlAGjn+kXpVbHpSifatC3zYyUo1WWRgF1/aQA8kRIAW6kF5YaFm9MUFHtljHEuPQBXri/WIfem0CsByTF2KS/W0gCkOrQAZq2fCIv53Ee9ch1uizyXLfVIuZ4GQE8P4LRIYGiFFshr08u1+idiCubV2nj9BJx6/aQH0IP69DQARlWBq3UUgNvQAkGZBoCcUeooYLdFixam4mnTpmH69OkBd5ubm4umTZuayux2O+Lj45Gbm+u33fXXX49WrVohNTUVP/zwA+69915s2rQJb7/9tupXn7gAUD9X1a8vQmbyQgghhDRGdu3aheho+WLtcrl81rvvvvvw6KOPVtnXxo0bAx7H+PHjle7WrRtSUlIwaNAgbN26FW3btg24X19w8kIIIYQEgbrKbRQdHW2avPjjzjvvxI033lhlnTZt2iA5ORn79u0zlVdWVuLQoUN+41l8kZ5+bJV+y5YtaNu2LZKTk7F27VpTnby8Y9tw1KRfgJMXQgghJDicYbdRYmIiEhMTT1kvIyMD+fn5yMnJQZ8+fQAAK1asgNfrVROS6rB+/XoAQEpKiur3oYcewr59+9RjqWXLliE6OhpdunTx141PGLBLCCGEEEXnzp0xZMgQjBs3DmvXrsVXX32FiRMnYsSIEcpptHv3bnTq1EmtpGzduhUPPvggcnJysH37drz33nsYNWoUzjvvPHTv3h0AcNFFF6FLly7485//jO+//x6ffPIJ7r//fkyYMMHvoy5/cPJCCCGEBIH6vEnd66+/jk6dOmHQoEG45JJLMGDAALz4ohhbKioqsGnTJuUmcjqd+Oyzz3DRRRehU6dOuPPOOzF8+HC8//77qo3NZsMHH3wAm82GjIwM3HDDDRg1apRpX5jqEjKPjW6N3YboKCuevVecREkvJyjtmSV1rbGyrX78IrPrpvwv++WHV8R5cbZL3Ce7MuW2zs7LVDoreZnS69d1V3rfFbJroWW7bPW/eLc4owDA6ZJnkNGbZd5Z0VKcR2E78qXfPrI8GKvtjBierKUB0Hz1XaIk/cC3MWanU5rjgNJl8TJDjtFcRaWxvtMAlEfLb5gX4hyyRIh7p0RLDxAdJlv3HzsmfcU6fW/3H6e5jY54xG0UZZW+9lbEKh3hEofWUY/uKDoxPYDuKpKxl1Zq6QE0u0CZR8r1bwaV2lb/Du2I7hCynrAlv980AH7aGH7cRv7Kq9wmv67SAPjtPxCnU03LmQaA1HPqcXqA+Pj4KjekS0tLg6H9QWjRogVWrVp1yn5btWqFjz76qNbj48oLIYQQQhoUIbPyQgghhNQn6sptFIpw8kIIIYQEA69x7FWb9iEKJy+EEEJIMKjHMS/1Hca8EEIIIaRBETIrL9duuQj2CBf+N2i2KrtpvDiPLhn+V6Vdw8Vt1PQVyV8EADOf0HIY/VHPYbRC6YF/+EHpz76RHEbzr/xK6YTvJIfRB8WybbKnQMq3bexsOnfnZJlmx/0qrpjCNHHdxP+4TemS5vFKGx7JHdS5qbiWSqIk8VDXsF+VXpPQ23TuVLu4dkoT5GMTbhW3UnksfOKJEpdOmSHjDo8Ux4+ev6hJWLGpfb6WkyjeKcf0HEZxdinXXUh6DqNfS2UHRz2HUbFH+tfzFwGnP4eR7kLS6wNnIIdRVd/a6iqHUT38ZhhQnEA9vI6aEsrxEfUVC2oZ81JnI2l4hMzkhRBCCKlXnOEddhsTfGxECCGEkAYFV14IIYSQIECrdOBw8kIIIYQEA7qNAoaPjQghhBDSoAiZlZfiOc1gd7ix/1lxj1hTkpRu+kK40q77diht+bc506Wew2jHZTL3m77rcqX/2eptpX/8WnIY7bzsiHS0abuSL28boHRMmOQ2ittgnluWtWmqdNjWg0rnpouLJqagUHRzcS5ZXeLA6R2zU+kvm3RTur0zT+nSRKkPAPGaq6g03k8Oo1j5GlCh5SqyRouzR89hFBeh5SMyxI0T75JywOweSnCIq6jAI+9ZrE3a7CiTXE/tXblKF1fqriJxPfnLXwTUXQ4jj0fPUyT3z1/+IuAM5DDyk78ICCCHUV3Vr7KvGp4jRAnlRwkNDYthwFKLoNvatG3ohMzkhRBCCKlXeP//qzbtQxQ+NiKEEEJIg4IrL4QQQkgQ4GOjwOHkhRBCCAkGdBsFTMhMXlxLc2C3OPCnzDtUmf0meWrW6oGvlX71ZdnG/7Jhd5n6eb/kG6Un/nGZ0i++PUTptLGy5X78agkYfelQhtLeEgkwPfidBOLGtZLgw4SfzIGrh86SANXENdJvaet4+KJ30u9K58bHKt097HulP2/aX+lUmwTTliSat7MPs0iwa2mCz9OhMlbal2mBuVFRR5U+oqUBSAovUjrfI0G5SS4JOj52LELpeLveRu5HG6ekPCjStvuPspRq5RJ0HG6VgN2SSil3nxiwqwXm6mkAyvVAXm0r/nItPYAeZOvRgmn1NAB+t/oH4PX4fqprmPqy+iw3N/BzgqoCXWuaBqCG/QQUsFtDQjUNAGlAcIfdgGHMCyGEEEIaFCGz8kIIIYTUJ7jDbuBw8kIIIYQEAz42Chg+NiKEEEJIg4IrL4QQQkgQsHiPvWrTPlQJmclL+eA+8Drc6PjELlU2bNl3Sr/91vlKOyzZSttHiIsFAO789k9Kb/jDAqU/Xi7tl18vjpPK3yTVwKIfzla6U1MZR+I6+QQWdhHnUPRX20znzr+yrdIJmlupbUvZ1t8WLU6nc2M2Kf1m8kA5t/OA0iXJ4syJs4rj52iifydKeZyMt8wQ1447VnP2aG6jppHiEDrolfMlucVVdMgbqXQTh9QHgIMeORZvl/QAG4+mKh3llnMXVsp1RFglNUFRhZTrrqKSSj09gOnUKDUd09xGmqvIprmKKvVyi+428p0GwOsnBQAAGH7+MPl1FcFPudd3Ogd//R87WIfOpbqCaQBMhHK8Q6OBj40Cho+NCCGEENKgCOrk5YsvvsBll12G1NRUWCwWLFmyxHTcMAxMnToVKSkpCAsLQ2ZmJjZv3hycwRJCCCF1iVEHrxAlqJOX4uJi9OjRA3PnzvV5/LHHHsOcOXMwb948rFmzBhERERg8eDBKS0t91ieEEEIaCsfTA9TmFaoENebl4osvxsUXX+zzmGEYmD17Nu6//35cccUVAIDXXnsNSUlJWLJkCUaMGHEmh0oIIYSQekK9jXnZtm0bcnNzkZmZqcpiYmKQnp6O7Oxsv+3KyspQWFhoehFCCCH1juMBu7V5hSj11m2Um3ssd09SUpKpPCkpSR3zxaxZszBjxoyTyt2374E9wgXjSnGrjIvZK+3+Ki6dm7ZdofTrXV419XPdlLuV3ttfcvbY1/6i9AO/DlM6xr1b6divxe1SdlZLqbNeHE3brk9WOvwdcQUBQFR7cSJZXdLX+U1/VfrL5G5K93Z/qvRrzcSxk2oTB01xisxfHRb5OJQ2Nf9SVGjuIUtCmdJFhrh5EqN1V5H0mxquuYq0fETJTinfXyn3P8lRYDr3jrImSqc59yudXyF9RVvlUeKRSt1V5FHa5CrSHhYfNZWbHS3llTbtmFxTRaWfHEYeP64iU7nFZ/mJ+Mth5Nd1o7mHTK6iABxCfi2YNXYh1bC8qnP4oc7yLdVT6CpqxBgw/d4G1D5EqbcrL4EyZcoUFBQUqNeuXbtO3YgQQgg5wzDmJXDq7eQlOfnYCkReXp6pPC8vTx3zhcvlQnR0tOlFCCGEkMZDvZ28tG7dGsnJyVi+fLkqKywsxJo1a5CRkRHEkRFCCCF1gIFaxrwE+wKCR1BjXoqKirBlyxb187Zt27B+/XrEx8ejZcuWmDRpEv7xj3+gffv2aN26NR544AGkpqZi2LBhwRs0IYQQUhdwh92ACerk5dtvv8UFF1ygfs7KygIAjB49Gq+88gruueceFBcXY/z48cjPz8eAAQOwdOlSuN1uf10SQgghpJET1MnL+eefD6OKmaPFYsHMmTMxc+bMWp/rzfafIDrKiq63T1BlD+w7qPSCIS8pPXn2zUo3uUecKAAQ/6ns8Hv3LcOUNsoOK31kRVOlo7uFKZ30tdT5/cI4pVP/J0HFnrPEdWOxiaMFAC5sIa6iDSktlD4vQqzjn7X8g9Jt7HJvjzSXtzrMIvmFSrTwIa8W9u5pIi4iwOwqahInrqJDHjlHi8h8pfd7IpRuGXZI6dzKWKVTnXI/dLdRV/fvpnOvrxRnVqxVcjrlV8i9jdJyGBWWy+Q2XLPNFFc4lXZreYdKK/T8ReYnqWWVct+sphxGUs+Uw8iPe8ifq8jw+HEUoYocRv5cRX7r+y6v0sVyul1FAeQpauyuIhKCeOE3JVm124co9TbmhRBCCGnM1Ge30aFDhzBy5EhER0cjNjYWY8eORVFRkd/627dvh8Vi8fl688035Zp9HF+8eHGNx1dv93khhBBCSHAYOXIk9u7di2XLlqGiogJjxozB+PHjsWjRIp/1W7Rogb1795rKXnzxRTz++OMn7aS/cOFCDBkyRP0cGxtb4/Fx8kIIIYQEg3oasLtx40YsXboU33zzDfr27QsAeOaZZ3DJJZfgiSeeQGpq6kltbDbbSduYvPPOO7jmmmsQGRlpKo+Nja1yy5PqwMdGhBBCSDCop+kBsrOzERsbqyYuAJCZmQmr1Yo1a9ZUq4+cnBysX78eY8eOPenYhAkT0KRJE/Tr1w8LFiyoMvbVH1x5IYQQQhowJ+bwc7lccLlcfmqfmtzcXDRt2tRUZrfbER8fX2V6Hp358+ejc+fO6N+/v6l85syZGDhwIMLDw/Hpp5/i1ltvRVFREW6//fYajTFkJi8v5KfBXWnHw39+TZVNfWGU0vdP/k7p1Le2KX3HyEGmfioPiHPmp4/PUbpl93ylm6+QD9LeP8gOvynPfav00fvOkk4NCRkf0naj0ltSzXmdLon5SOl1bXsp3cUpeX0KWoujJtoqbpzi5tKP7iqqSBWXTpFXchYlJB4xnfuAR9q0jpF7sNsjLqE24ZKLaXeFuKmaO6V+XkWM0h3d8nx0Q0kzOXeEOSjsULk4l0yuogrfrqIiP66iEq1cdxWV+clfBJhzGPlzFVUvh5FvV5FfhxBgcgmdFldRVeeuy1xFNSRUXUXMYRSC1NFjoxYtWpiKp02bhunTp59U/b777sOjjz5aZZcbN26s8nh1OHr0KBYtWoQHHnjgpGN6Wa9evVBcXIzHH3+ckxdCCCGkQVBHVuldu3aZUuH4W3W58847ceONN1bZZZs2bZCcnIx9+/aZyisrK3Ho0KFqxaq89dZbKCkpwahRo05ZNz09HQ8++CDKyspqtFrEyQshhBASBGprdz7etrp5/BITE5GYmHjKehkZGcjPz0dOTg769OkDAFixYgW8Xi/S09NP2X7+/Pm4/PLLq3Wu9evXIy4ursaPuTh5IYQQQoiic+fOGDJkCMaNG4d58+ahoqICEydOxIgRI5TTaPfu3Rg0aBBee+019OvXT7XdsmULvvjiC3z00Ucn9fv+++8jLy8P55xzDtxuN5YtW4aHH34Yd911V43HyMkLIYQQEgzqqVUaAF5//XVMnDgRgwYNgtVqxfDhwzFnzhx1vKKiAps2bUJJSYmp3YIFC9C8eXNcdNFFJ/XpcDgwd+5cTJ48GYZhoF27dnjyyScxbty4Go8vZCYvry+4EDaXG/+75ylV9tKrst3+qGGyYY4nT571rX3LvETW7GwJZG31vp/t/p/+Rumj93RR2nhWPmhXdv5e6Q1asNU18UuUntrR/Ib2dsqH5HB7WWKLs4YrfSRN6uuBueXNJRi3wCsBvk2bFiid5/Eo3SFuv+ncemBuh8g8pXdVJCjd2i1tfq+IV1rf7v+XoylKD4jYpPSBMtkHINYqYwWAQ2VyfVEWGWNBmQTs6oG5xeVyb/TA3NIK+bjrgbnlWrkelAsAlVrArh406zGVa4G5lX4Ccz2nDrK1orrpAeooMLfK9AA1Lfd3jgBSE/ijEQS0MiiXmPAatftQeE/fByo+Pt7vhnQAkJaW5tPi/PDDD+Phhx/22WbIkCGmzelqA/d5IYQQQkiDImRWXgghhJB6RT1+bFTf4eSFEEIICQq13SU3dCcvfGxECCGEkAYFV14IIYSQYMDHRgETMpOXpIXrYbc4MWDQjaostVRcMNsWdFU6clCF0i3/s8vUz9ax4gxqNfVrpcNmtVfa8pzc1r92/Z/Sn7WXJFd/jpM0BRN73KF0b5ec+8BZsp09YN7uv6CDfGgrjEqlva3FkXTAIzqtmWzd/7vmiOneZI/SWys1h1CUlAPAb+WS56KdW9xGO8qaKJ0esUXpdUWtlM6M/Fnp3FLZSCnBKq6nA2V6CgBxSQHA4TK57girLBYe0VxFbou4f46WO5TWXUVluttIq19RIdoO0QDgqfSz3b8/V5HXz2JmTbf6P6GNjsXrs7jmrqKA3EY12w60sbuK/F0fXUWkWngN1OoDfxrdRvUdPjYihBBCSIMiZFZeCCGEkHqF4TUl5g2ofYjCyQshhBASDBjzEjCcvBBCCCHBgDEvAcOYF0IIIYQ0KEJm5cXSpgUsNheSHhWHSt4N3ZRu+sp3Sm96XvIRtR+z29TPeUMOKb17nuTpmdHpPaX/efb1So+OWa704ozBSndzioNmX29xuIRZZHxHuonzCACKtJxE0e0lr9JOj5T3bCHj3VwhOYH6Ndmh9IbyVKV7R0n5z6XNlO7sNl/310UdlL4iJkfplfkdlb4yZp3Se4/GKJ2g5SrKLZEcSVHa1PnwURlruMXs+DlSKvfEpbmBSkrFjaXnMCor9+0qqtTKdeeQR3Mb6c4hAPBWWH0eMzy+5/1GpR9XkZ/cRhZ/OY8QgKvIb30/5VW6jWqWk8ivuyYQp1M9hO4hclrgY6OACZnJCyGEEFKvMFDLyUudjaTBwcdGhBBCCGlQcOWFEEIICQZ8bBQwnLwQQgghwcDrhf+gtOq2D0342IgQQgghDYqQWXn59Y5IWMPcaD9GXEV9ZovjZ/e7sUo/94d/Kf3PP4hzCAD+kfKs0pcOuUvpIWHlSk8eJP0mWCVnz4FzJQeR7hwK6y0Opq2VRTK+jttN5/6+XNw1FzX/RelvSiXf0qCEjUqvOdpW6XMiJe+QP+fQgv1/UHpw8gbTuf+vqL/SzRKKld5+RPIhJaTKt4C9RZLDKMYqbp5DJeIqirTIx6+gxK20+wS30VGTq0iOlZdJDiM9J1FFmW+3kafcj3Oo0v8c3jDlNtLb+HYV+XMP+XUVVeE28nuspu4hf86hKvIqNWb3UFXOIbqKyBmFj40CJmQmL4QQQki9gpOXgOFjI0IIIYQ0KLjyQgghhAQDpgcIGE5eCCGEkCBgGF4YtcgMXZu2DZ2Qmbws/ePziIqy4k8j71Zl85o/p3S3v0xQWg++vfV6CQoFgCir/Hz0skKld1YeUbrD+b8pvbpMgnSv6CnBwp8cTVR6TNtspT8qktQEI5LWmM79YWFPpYfGfK/0/x2QYNrbmko6gpm/Xybnbvmj0s/vOl/prCb/U/qXw0lKJzczz+i35Utgbnxr+djkFcp2/zHavTlUpG/3L+VFxRKY69ICdsuOOrRy8z0v146ZAnDLfG/rb5RLuSnItsJ38C3K/ZQDsFT6CXb1F+Rb08Dcqv72+Amo9Rdo67/cT/9Vpgeo4liQqGlqAgbfknqPYdRu9YQxL4QQQgghDYOQWXkhhBBC6hVGLWNeQnjlhZMXQgghJBh4vVU8160GIRzzwsdGhBBCCGlQcOWFEEIICQZ8bBQwITN5yfO4UOyxos+kdarsw5JIpceM+FTpeQXNlH544Fumfh450Fvp2d3fkPK8TKVntHpX6Vm/D5W+Wkr5xK3XKP1yO+lnxM+jlF5ylqQpAIBZmy5W+r6e4kS6dXea0k83k8W0n3KTlW7e2qX0tn0JSid0FPdP3gHZ0j/aIvUBIP+QpDmItMqxkkJJhaC7hMoLXT7LK4t055B8/DwlUn6i4wdHfbuHUOqnvMz3gqKl3M9CY0UVW/RX+Gnj14VUs/QAftMGIACX0Bn4O1ZXjh9u0U8IYHi9MGrx2CiUrdJ8bEQIIYSQBkXIrLwQQggh9Qo+NgoYTl4IIYSQYOA1avecNIQnL3xsRAghhJAGBVdeCCGEkGBgGKg6R0h12ocmITN5GfPhX2F1u7HlmhdUWbv//FXp6pQDQLuPxCU045oNSv/1q55KP3eNOIFyvm2ndNs24m7a/H0LpZt1lPxAezc2VTqhmzh8ACB/S5zS0b3F5XN0h7QPSxeXT8Xv0l53/Hhzpa3u+MEBl+9yANbDvt1A1ny77/Ij4gTSsRX7KS/xvwhoLfV9zFrm26ljLfdT7sdVZPXjEAIAq6dm5f6MA37LA3Dd0PFDSOPA8BowavHLZnDyQgghhJAziuFF7VZeaJWu18ydOxdpaWlwu91IT0/H2rVrgz0kQgghpNHy0EMPoX///ggPD0dsbGy12hiGgalTpyIlJQVhYWHIzMzE5s2bTXUOHTqEkSNHIjo6GrGxsRg7diyKiopqPL56P3l54403kJWVhWnTpmHdunXo0aMHBg8ejH379gV7aIQQQkjAGF6j1q/TRXl5Oa6++mrccsst1W7z2GOPYc6cOZg3bx7WrFmDiIgIDB48GKWlparOyJEjsWHDBixbtgwffPABvvjiC4wfP77G46v3k5cnn3wS48aNw5gxY9ClSxfMmzcP4eHhWLBgQbCHRgghhASO4a396zQxY8YMTJ48Gd26davepRgGZs+ejfvvvx9XXHEFunfvjtdeew179uzBkiVLAAAbN27E0qVL8fLLLyM9PR0DBgzAM888g8WLF2PPnj01Gl+9jnkpLy9HTk4OpkyZosqsVisyMzORnZ3ts01ZWRnKysrUzwUFBQAA7/+f+RUekUhLrzYbrE55IG3qqpzn5rl5bp6b5z795y4sOjYhOBPBsJWoqNUedZWoAAAUFhaayl0uF1wul68mp41t27YhNzcXmZmSKicmJgbp6enIzs7GiBEjkJ2djdjYWPTt21fVyczMhNVqxZo1a3DllVdW/4RGPWb37t0GAOPrr782ld99991Gv379fLaZNm3a8S0L+eKLL7744iug165du07b/21Hjx41kpOT62SckZGRJ5VNmzatzsa6cOFCIyYm5pT1vvrqKwOAsWfPHlP51VdfbVxzzTWGYRjGQw89ZHTo0OGktomJicZzzz1Xo3HV65WXQJgyZQqysrLUz/n5+WjVqhV27tyJmJiYII7szFJYWIgWLVpg165diI6OPnWDRgKvm9cdCvC6T991G4aBI0eOIDU19bT0DwButxvbtm1DeXl5rfsyDAMWi3m7B3+rLvfddx8effTRKvvbuHEjOnXqVOtxnW7q9eSlSZMmsNlsyMvLM5Xn5eUhOTnZZxt/y2UxMTEh9Ut+nOjoaF53CMHrDi143aeHM/FF1+12w+12n/bz6Nx555248cYbq6zTpk2bgPo+/n9yXl4eUlJSVHleXh569uyp6pxotqmsrMShQ4f8/p/uj3o9eXE6nejTpw+WL1+OYcOGAQC8Xi+WL1+OiRMnBndwhBBCSAMiMTERiYmJp6Xv1q1bIzk5GcuXL1eTlcLCQqxZs0Y5ljIyMpCfn4+cnBz06dMHALBixQp4vV6kp6fX6Hz13m2UlZWFl156Ca+++io2btyIW265BcXFxRgzZkywh0YIIYQ0Snbu3In169dj586d8Hg8WL9+PdavX2/ak6VTp0545513AAAWiwWTJk3CP/7xD7z33nv48ccfMWrUKKSmpqrFh86dO2PIkCEYN24c1q5di6+++goTJ07EiBEjavyYrl6vvADAtddei/3792Pq1KnIzc1Fz549sXTpUiQlJVWrvcvlwrRp08545HWw4XXzukMBXjevm5wepk6dildffVX93KtXLwDA559/jvPPPx8AsGnTJuXoBYB77rkHxcXFGD9+PPLz8zFgwAAsXbrU9Hjs9ddfx8SJEzFo0CBYrVYMHz4cc+bMqfH4LIYRwskRCCGEENLgqPePjQghhBBCdDh5IYQQQkiDgpMXQgghhDQoOHkhhBBCSIOiUU9e5s6di7S0NLjdbqSnp2Pt2rXBHlKdMmvWLJx99tmIiopC06ZNMWzYMGzatMlUp7S0FBMmTEBCQgIiIyMxfPjwkzb9a+g88sgjyqZ3nMZ63bt378YNN9yAhIQEhIWFoVu3bvj222/VcaMaKekbGh6PBw888ABat26NsLAwtG3bFg8++KAp90xjuO4vvvgCl112GVJTU2GxWFQyu+NU5xoPHTqEkSNHIjo6GrGxsRg7dqzJ2lofqeq6KyoqcO+996Jbt26IiIhAamoqRo0adVISv4Z43aR2NNrJyxtvvIGsrCxMmzYN69atQ48ePTB48OCTdvdryKxatQoTJkzA6tWrsWzZMlRUVOCiiy5CcXGxqjN58mS8//77ePPNN7Fq1Srs2bMHV111VRBHXbd88803eOGFF9C9e3dTeWO87sOHD+Pcc8+Fw+HAxx9/jJ9//hn//Oc/ERcXp+pUJyV9Q+PRRx/F888/j2effRYbN27Eo48+isceewzPPPOMqtMYrru4uBg9evTA3LlzfR6vzjWOHDkSGzZswLJly/DBBx/giy++wPjx48/UJQREVdddUlKCdevW4YEHHsC6devw9ttvY9OmTbj88stN9RridZNaUqNMSA2Ifv36GRMmTFA/ezweIzU11Zg1a1YQR3V62bdvnwHAWLVqlWEYhpGfn284HA7jzTffVHU2btxoADCys7ODNcw648iRI0b79u2NZcuWGX/84x+NO+64wzCMxnvd9957rzFgwAC/x71er5GcnGw8/vjjqiw/P99wuVzGv//97zMxxNPC0KFDjb/85S+msquuusoYOXKkYRiN87oBGO+88476uTrX+PPPPxsAjG+++UbV+fjjjw2LxWLs3r37jI29Npx43b5Yu3atAcDYsWOHYRiN47pJzWmUKy/l5eXIyckxpea2Wq3IzMxEdnZ2EEd2ejm+WVB8fDwAICcnBxUVFab70KlTJ7Rs2bJR3IcJEyZg6NChpusDGu91v/fee+jbty+uvvpqNG3aFL169cJLL72kjp8qJX1DpX///li+fDl+/fVXAMD333+PL7/8EhdffDGAxnvdOtW5xuzsbMTGxqJv376qTmZmJqxWK9asWXPGx3y6KCgogMViQWxsLIDQuW5ipt7vsBsIBw4cgMfjOWkX3qSkJPzyyy9BGtXpxev1YtKkSTj33HPRtWtXAEBubi6cTqf6JT9OUlIScnNzgzDKumPx4sVYt24dvvnmm5OONdbr/u233/D8888jKysLf/vb3/DNN9/g9ttvh9PpxOjRo9W1+frcN+Trvu+++1BYWIhOnTrBZrPB4/HgoYcewsiRIwGg0V63TnWuMTc3F02bNjUdt9vtiI+PbzT3obS0FPfeey+uu+46lZgxFK6bnEyjnLyEIhMmTMBPP/2EL7/8MthDOe3s2rULd9xxB5YtW3bGs7IGE6/Xi759++Lhhx8GcGy77p9++gnz5s3D6NGjgzy608d//vMfvP7661i0aBHOOussrF+/HpMmTUJqamqjvm5ipqKiAtdccw0Mw8Dzzz8f7OGQINMoHxs1adIENpvtJHdJXl5ejdNuNwQmTpyIDz74AJ9//jmaN2+uypOTk1FeXo78/HxT/YZ+H3JycrBv3z707t0bdrsddrsdq1atwpw5c2C325GUlNQorzslJQVdunQxlXXu3Bk7d+4EYE5Jr9PQr/vuu+/GfffdhxEjRqBbt27485//jMmTJ2PWrFkAGu9161TnGpOTk08yJFRWVuLQoUMN/j4cn7js2LEDy5YtU6suQOO+buKfRjl5cTqd6NOnD5YvX67KvF4vli9fjoyMjCCOrG4xDAMTJ07EO++8gxUrVqB169am43369IHD4TDdh02bNmHnzp0N+j4MGjQIP/74o8pyun79evTt2xcjR45UujFe97nnnnuSFf7XX39Fq1atAJhT0h/neEr6hnzdJSUlsFrNf6psNhu8Xi+AxnvdOtW5xoyMDOTn5yMnJ0fVWbFiBbxeL9LT08/4mOuK4xOXzZs347PPPkNCQoLpeGO9bnIKgh0xfLpYvHix4XK5jFdeecX4+eefjfHjxxuxsbFGbm5usIdWZ9xyyy1GTEyMsXLlSmPv3r3qVVJSourcfPPNRsuWLY0VK1YY3377rZGRkWFkZGQEcdSnB91tZBiN87rXrl1r2O1246GHHjI2b95svP7660Z4eLjxr3/9S9V55JFHjNjYWOPdd981fvjhB+OKK64wWrdubRw9ejSII68do0ePNpo1a2Z88MEHxrZt24y3337baNKkiXHPPfeoOo3huo8cOWJ89913xnfffWcAMJ588knju+++U66a6lzjkCFDjF69ehlr1qwxvvzyS6N9+/bGddddF6xLqhZVXXd5eblx+eWXG82bNzfWr19v+jtXVlam+miI101qR6OdvBiGYTzzzDNGy5YtDafTafTr189YvXp1sIdUpwDw+Vq4cKGqc/ToUePWW2814uLijPDwcOPKK6809u7dG7xBnyZOnLw01ut+//33ja5duxoul8vo1KmT8eKLL5qOe71e44EHHjCSkpIMl8tlDBo0yNi0aVOQRls3FBYWGnfccYfRsmVLw+12G23atDH+/ve/m/7zagzX/fnnn/v8fR49erRhGNW7xoMHDxrXXXedERkZaURHRxtjxowxjhw5EoSrqT5VXfe2bdv8/p37/PPPVR8N8bpJ7bAYhrZNJSGEEEJIPadRxrwQQgghpPHCyQshhBBCGhScvBBCCCGkQcHJCyGEEEIaFJy8EEIIIaRBwckLIYQQQhoUnLwQQgghpEHByQshpFps374dFosF69evD/ZQCCEhDicvhDQQbrzxRlgsFlgsFjgcDiQlJeHCCy/EggULVJ6fujzXsGHD6rRPQgipKzh5IaQBMWTIEOzduxfbt2/Hxx9/jAsuuAB33HEHLr30UlRWVgZ7eIQQckbg5IWQBoTL5UJycjKaNWuG3r17429/+xveffddfPzxx3jllVcAAPn5+bjpppuQmJiI6OhoDBw4EN9//73qY/r06ejZsydeeOEFtGjRAuHh4bjmmmtQUFCgjr/66qt499131UrPypUrVfvffvsNF1xwAcLDw9GjRw9kZ2efyVtACCGcvBDS0Bk4cCB69OiBt99+GwBw9dVXY9++ffj444+Rk5OD3r17Y9CgQTh06JBqs2XLFvznP//B+++/j6VLl+K7777DrbfeCgC46667cM0116hVnr1796J///6q7d///nfcddddWL9+PTp06IDrrruOqz6EkDMKJy+ENAI6deqE7du348svv8TatWvx5ptvom/fvmjfvj2eeOIJxMbG4q233lL1S0tL8dprr6Fnz54477zz8Mwzz2Dx4sXIzc1FZGQkwsLC1CpPcnIynE6nanvXXXdh6NCh6NChA2bMmIEdO3Zgy5YtwbhsQkiIwskLIY0AwzBgsVjw/fffo6ioCAkJCYiMjFSvbdu2YevWrap+y5Yt0axZM/VzRkYGvF4vNm3adMpzde/eXemUlBQAwL59++rwagghpGrswR4AIaT2bNy4Ea1bt0ZRURFSUlJMMSrHiY2NrZNzORwOpS0WCwDUuduJEEKqgpMXQho4K1aswI8//ojJkyejefPmyM3Nhd1uR1pamt82O3fuxJ49e5CamgoAWL16NaxWKzp27AgAcDqd8Hg8Z2L4hBBSYzh5IaQBUVZWhtzcXHg8HuTl5WHp0qWYNWsWLr30UowaNQpWqxUZGRkYNmwYHnvsMXTo0AF79uzBhx9+iCuvvBJ9+/YFALjdbowePRpPPPEECgsLcfvtt+Oaa65BcnIyACAtLQ2ffPIJNm3ahISEBMTExATzsgkhxAQnL4Q0IJYuXYqUlBTY7XbExcWhR48emDNnDkaPHg2r9VgI20cffYS///3vGDNmDPbv34/k5GScd955SEpKUv20a9cOV111FS655BIcOnQIl156KZ577jl1fNy4cVi5ciX69u2LoqIifP7551Wu5BBCyJnEYhiGEexBEELOHNOnT8eSJUu4zT8hpMFCtxEhhBBCGhScvBBCCCGkQcHHRoQQQghpUHDlhRBCCCENCk5eCCGEENKg4OSFEEIIIQ0KTl4IIYQQ0qDg5IUQQgghDQpOXgghhBDSoODkhRBCCCENCk5eCCGEENKg4OSFEEIIIQ2K/wcnumZWHU5nbgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class TransformerEmbedding(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.hidden_size = config[\"d_model\"]  # 词向量维度\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "\n",
    "        # layers,设置padding_idx可以让pad的词向量全为0\n",
    "        self.word_embedding = nn.Embedding(\n",
    "            self.vocab_size, self.hidden_size, padding_idx=self.pad_idx\n",
    "        )\n",
    "        self.position_embedding = nn.Embedding(\n",
    "            self.max_length, self.hidden_size,\n",
    "            # 位置编码，权重通过get_positional_encoding函数计算得到\n",
    "            _weight=self.get_position_encoding(self.max_length, self.hidden_size)\n",
    "        )\n",
    "\n",
    "        self.position_embedding.weight.requires_grad_(False)  # 不更新位置编码的权重\n",
    "        self.dropout = nn.Dropout(dropout_rate)  # 随机失活层\n",
    "\n",
    "    def get_word_embedding_weight(self):\n",
    "        return self.word_embedding.weight\n",
    "\n",
    "    # 计算位置信息\n",
    "    # 用于定义类方法（class method）。\n",
    "    # 类方法是绑定到类而不是实例的方法，可以通过类本身或类的实例调用。\n",
    "    @classmethod\n",
    "    def get_position_encoding(self, max_length, hidden_size):\n",
    "        # max_length是最大长度，hidden_size是embedding维度相等\n",
    "        pe = torch.zeros(max_length, hidden_size)  # 初始化位置编码\n",
    "        # .unsqueeze(1) 是将这个一维张量转换为二维张量，\n",
    "        # 即将其形状从 (max_length,) 变为 (max_length, 1)。\n",
    "        # 这个操作在张量的维度上增加了一个维度，使其从一维变为二维，第二维的大小为 1。\n",
    "        position = torch.arange(0, max_length).unsqueeze(1)  # 位置信息,从0到max_length-1\n",
    "        # 计算位置编码的权重,为了性能考量（是数学上的对数函数分解），这里用了log函数\n",
    "        # torch.arange(0, hidden_size, 2) 在 PyTorch 中用于创建一个一维张量，包含从 0 开始到（但不包括）hidden_size 的数值，步长为 2\n",
    "        div_term = torch.exp(\n",
    "            torch.arange(0, hidden_size, 2) *\n",
    "            -(torch.log(torch.Tensor([10000.0])) / hidden_size)\n",
    "        )\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)  # 偶数位置\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)  # 奇数位置\n",
    "        return pe\n",
    "\n",
    "    def forward(self, input_ids):\n",
    "        # input_ids: [batch_size,seq_len]\n",
    "        seq_len = input_ids.shape[1]\n",
    "        assert (\n",
    "                seq_len <= self.max_length), f\"input sequence length should no more than {self.max_length} but got {seq_len}\"\n",
    "\n",
    "        position_ids = torch.arange(seq_len, dtype=torch.long, device=input_ids.device)  # 位置信息\n",
    "        # unsqueeze(0): 在第一维添加一个维度，使张量的形状从 [seq_len] 变为 [1, seq_len]\n",
    "        # expand_as(input_ids): 扩展张量的形状，使其与 input_ids 张量的形状相同\n",
    "        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "\n",
    "        # print(input_ids.shape) #为了调试\n",
    "        # print(position_ids.shape) #为了调试\n",
    "        # print(position_ids) #为了调试\n",
    "\n",
    "        # embedding层\n",
    "        word_embeds = self.word_embedding(input_ids)  # 词嵌入\n",
    "        # word_embeds: [batch_size,seq_len,hidden_size]\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "        pos_embeds = self.position_embedding(position_ids)  # 位置嵌入 # ？\n",
    "        # pos_embeds: [batch_size,seq_len,hidden_size]   \n",
    "        embeds = word_embeds + pos_embeds  # 相加得到嵌入向量\n",
    "        # embeds: [batch_size,seq_len,hidden_size]\n",
    "        embeds = self.dropout(embeds)  # 随机失活\n",
    "        return embeds\n",
    "\n",
    "\n",
    "# #随机input，调用TransformerEmbedding\n",
    "# config={\n",
    "#     \"vocab_size\": 100,\n",
    "#     \"d_model\": 128,\n",
    "#     \"pad_idx\": 0,\n",
    "#     \"max_length\": 64,\n",
    "#     \"dropout\": 0.1,\n",
    "# }\n",
    "# input_ids = torch.randint(0, 100, (2, 50))\n",
    "# embeds = TransformerEmbedding(config)(input_ids)\n",
    "# embeds.shape\n",
    "\n",
    "# 绘制位置编码\n",
    "def plot_position_embedding(position_embedding):\n",
    "    plt.pcolormesh(position_embedding)  # 绘制位置编码矩阵\n",
    "    plt.xlabel('Depth')\n",
    "    plt.ylabel('Position')\n",
    "    plt.colorbar()  # 颜色条，-1到1的颜色范围\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "position_embedding = TransformerEmbedding.get_position_encoding(64, 128)\n",
    "plot_position_embedding(position_embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39630e37b910bd7f",
   "metadata": {},
   "source": [
    "## Transformer Block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e7edad4645f763",
   "metadata": {},
   "source": [
    "### scaled-dot-product-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bd08ede5d628f45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.834753Z",
     "start_time": "2025-02-06T14:04:29.821751Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:21.352524Z",
     "iopub.status.busy": "2025-02-07T09:47:21.352211Z",
     "iopub.status.idle": "2025-02-07T09:47:21.553564Z",
     "shell.execute_reply": "2025-02-07T09:47:21.552703Z",
     "shell.execute_reply.started": "2025-02-07T09:47:21.352501Z"
    }
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Optional, Tuple\n",
    "\n",
    "Tensor = torch.Tensor\n",
    "\n",
    "\n",
    "# 修饰器 @dataclass 会自动生成 __init__、__repr__ 和 __eq__ 等方法，减少了样板代码的编写\n",
    "@dataclass\n",
    "class AttentionOutput:\n",
    "    hidden_states: Tensor\n",
    "    attn_scores: Tensor\n",
    "\n",
    "\n",
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        assert (\n",
    "                self.hidden_size % self.num_heads == 0\n",
    "        ), \"Hidden size must be divisible by num_heads but got {} and {}\".format(\n",
    "            self.hidden_size, self.num_heads)\n",
    "        self.head_dim = self.hidden_size // self.num_heads  # 每个头的维度\n",
    "\n",
    "        # layers\n",
    "        # 第二个self.hidden_size可以*系数\n",
    "        self.Wq = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wk = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wv = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wo = nn.Linear(self.hidden_size, self.hidden_size, bias=False)  # 输出层\n",
    "\n",
    "    # -> Tensor 是一种类型注解，表示函数的返回值类型是 Tensor\n",
    "    def _split_heads(self, x: Tensor) -> Tensor:\n",
    "        # 若hidden_size是512\n",
    "        bs, seq_len, _ = x.shape  # [batch_size,seq_len,hidden_size]\n",
    "        # 先将x变形为[batch_size,seq_len,num_heads,head_dim]\n",
    "        x = x.view(bs, seq_len, self.num_heads, self.head_dim)\n",
    "        # num_heads是8，head_dim是64\n",
    "        return x.permute(0, 2, 1, 3)\n",
    "        # 变换维度，[batch_size, num_heads, seq_len, head_dim]\n",
    "\n",
    "    def _merge_heads(self, x: Tensor) -> Tensor:\n",
    "        # 将多头注意力的输出合并为一个张量\n",
    "        bs, _, seq_len, _ = x.shape  # [batch_size, num_heads, seq_len, head_dim]\n",
    "        # 变换维度，变为[batch_size, seq_len, hidden_size]\n",
    "        return x.permute(0, 2, 1, 3).reshape(bs, seq_len, self.hidden_size)\n",
    "\n",
    "    def forward(self, querys, keys, values, attn_mask=None) -> AttentionOutput:\n",
    "        # split heads\n",
    "        # [batch_size, seq_len,hidden_dim]->[batch_size, num_heads, seq_len, head_dim]\n",
    "        querys = self._split_heads(self.Wq(querys))\n",
    "        keys = self._split_heads(self.Wk(keys))\n",
    "        values = self._split_heads(self.Wv(values))\n",
    "\n",
    "        # calculate attention scores\n",
    "        # 计算注意力分数，matmul是矩阵乘法(在后两个维度上进行)，mT是矩阵转置,\n",
    "        # qk_logits是[batch_size, num_heads, seq_len, seq_len]\n",
    "        qk_logits = torch.matmul(querys, keys.mT)\n",
    "        if attn_mask is not None:\n",
    "            # seq_len*seq_len的部分是有效的\n",
    "            attn_mask = attn_mask[:, :, :querys.shape[-2], :keys.shape[-2]]  #切片\n",
    "            qk_logits += attn_mask * -1e9  # 给需要mask的地方设置一个负无穷\n",
    "        # 计算注意力数,softmax是在seq_len维度上进行的\n",
    "        attn_scores = F.softmax(qk_logits / (self.head_dim ** 0.5), dim=-1)\n",
    "        # attn_scores: [batch_size, num_heads, seq_len, seq_len]\n",
    "\n",
    "        # softmax后的结果与value相乘，得到新的表示\n",
    "        embeds = torch.matmul(attn_scores, values)\n",
    "        # embeds的尺寸是[batch_size, num_heads, seq_len, head_dim]\n",
    "        # _merge_heads(embeds)的输出是[batch_size, seq_len, hidden_size]\n",
    "        embeds = self.Wo(self._merge_heads(embeds))  # 输出层\n",
    "        # embeds: [batch_size, seq_len, hidden_size]\n",
    "\n",
    "        return AttentionOutput(hidden_states=embeds, attn_scores=attn_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9eb177f33d1050d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.843757Z",
     "start_time": "2025-02-06T14:04:29.835755Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:21.554734Z",
     "iopub.status.busy": "2025-02-07T09:47:21.554415Z",
     "iopub.status.idle": "2025-02-07T09:47:21.562145Z",
     "shell.execute_reply": "2025-02-07T09:47:21.561437Z",
     "shell.execute_reply.started": "2025-02-07T09:47:21.554712Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key_value.shape torch.Size([2, 4, 2])\n",
      "torch.Size([2, 3, 2])\n",
      "torch.Size([2, 2, 3, 4])\n"
     ]
    }
   ],
   "source": [
    "mha = MultiHeadAttention({\"num_heads\": 2, \"d_model\": 2})\n",
    "query = torch.randn(2, 3, 2)  # [batch_size, seq_len, hidden_size]\n",
    "query /= query.norm(dim=-1, keepdim=True)  # 归一化\n",
    "key_value = torch.randn(2, 4, 2)\n",
    "print(f'key_value.shape {key_value.shape}')\n",
    "outputs = mha(query, key_value, key_value)  #最终输出shape和query的shape一样\n",
    "print(outputs.hidden_states.shape)\n",
    "print(outputs.attn_scores.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e37f29d98cf51bac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.852319Z",
     "start_time": "2025-02-06T14:04:29.845757Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:21.563369Z",
     "iopub.status.busy": "2025-02-07T09:47:21.563030Z",
     "iopub.status.idle": "2025-02-07T09:47:21.571356Z",
     "shell.execute_reply": "2025-02-07T09:47:21.570582Z",
     "shell.execute_reply.started": "2025-02-07T09:47:21.563335Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.5631, 0.0763, 0.3606],\n",
      "        [0.3406, 0.2957, 0.3637]])\n"
     ]
    }
   ],
   "source": [
    "#随机一个2阶张量，在-1维度计算softmax\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = torch.randn(2, 3)\n",
    "x_softmax = F.softmax(x, dim=-1)\n",
    "print(x_softmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b451e28eb10aaf26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.232694Z",
     "start_time": "2025-02-06T14:04:30.002057Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:21.572390Z",
     "iopub.status.busy": "2025-02-07T09:47:21.572119Z",
     "iopub.status.idle": "2025-02-07T09:47:21.811986Z",
     "shell.execute_reply": "2025-02-07T09:47:21.811178Z",
     "shell.execute_reply.started": "2025-02-07T09:47:21.572371Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplots() 用于创建子图网格，其维度基于 outputs.attn_scores.shape[:2]。子图的行数和列数似乎由 outputs.attn_scores 的前两个维度确定。\n",
    "fig, axis = plt.subplots(*outputs.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs.attn_scores.shape[1]):\n",
    "        # axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())：此行使用 Matplotlib 的 matshow 绘制每个 i 和 j 的注意力分数热图。detach().numpy() 将 PyTorch 张量转换为 NumPy 数组以进行可视化。\n",
    "        axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs.attn_scores.shape[2]):\n",
    "            for y in range(outputs.attn_scores.shape[3]):\n",
    "                # axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")：此代码在热图上叠加文本，显示 (x, y) 位置处的注意力分数。格式化部分 f\"{outputs.attn_scores[i, j, x, y]:.2f}\" 确保以两位小数显示注意力分数。文本以白色居中显示在 (y, x) 坐标处。\n",
    "                axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")\n",
    "fig.suptitle(\"multi head attention without mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42dc1608bbcd426c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.480546Z",
     "start_time": "2025-02-06T14:04:30.233690Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:21.813259Z",
     "iopub.status.busy": "2025-02-07T09:47:21.812859Z",
     "iopub.status.idle": "2025-02-07T09:47:22.050467Z",
     "shell.execute_reply": "2025-02-07T09:47:22.049807Z",
     "shell.execute_reply.started": "2025-02-07T09:47:21.813231Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask\n",
    "mask = torch.Tensor([[0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]).reshape(1, 1, 3, 4)  #手工构造mask\n",
    "outputs_masked = mha(query, key_value, key_value, mask)\n",
    "\n",
    "fig, axis = plt.subplots(*outputs_masked.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs_masked.attn_scores.shape[1]):\n",
    "        axis[i, j].matshow(outputs_masked.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs_masked.attn_scores.shape[2]):\n",
    "            for y in range(outputs_masked.attn_scores.shape[3]):\n",
    "                axis[i, j].text(y, x, f\"{outputs_masked.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\",\n",
    "                                color=\"w\")\n",
    "fig.suptitle(\"multi head attention with mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd2b7a6a479f877c",
   "metadata": {},
   "source": [
    "### Transformer-Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2247e4861550df31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.489814Z",
     "start_time": "2025-02-06T14:04:30.481541Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:22.051665Z",
     "iopub.status.busy": "2025-02-07T09:47:22.051337Z",
     "iopub.status.idle": "2025-02-07T09:47:22.063620Z",
     "shell.execute_reply": "2025-02-07T09:47:22.062800Z",
     "shell.execute_reply.started": "2025-02-07T09:47:22.051644Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerBlockOutput:\n",
    "    # 用于存储某个块产生的隐藏状态。\n",
    "    hidden_states: Tensor\n",
    "    # 包含了自注意力机制（self-attention）所计算得到的注意力分数\n",
    "    self_attn_scores: Tensor\n",
    "    # 是一个可选字段，存储了交叉注意力（cross-attention）计算得到的注意力分数。\n",
    "    # 这里的 Optional 表示这个字段可以是 Tensor 类型，也可以是 None。\n",
    "    cross_attn_scores: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, config, add_cross_attention=False):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        dropout_rate = config[\"dropout\"]  # 随机失活率\n",
    "        ffn_dim = config[\"dim_feedforward\"]  # 前馈网络的维度\n",
    "        eps = config[\"layer_norm_eps\"]  # 层归一化的epsilon值\n",
    "\n",
    "        # self-attention\n",
    "        self.self_attn = MultiHeadAttention(config)  # 多头注意力\n",
    "        self.self_ln = nn.LayerNorm(self.hidden_size, eps=eps)  # 层归一化(层标准化)\n",
    "        self.self_dropout = nn.Dropout(dropout_rate)  # 随机失活\n",
    "\n",
    "        # cross-attention，交叉注意力，decoder中使用,因此额外做一个判断\n",
    "        if add_cross_attention:\n",
    "            self.cross_attn = MultiHeadAttention(config)\n",
    "            self.cross_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "            self.cross_dropout = nn.Dropout(dropout_rate)\n",
    "        else:\n",
    "            self.cross_attn = None\n",
    "\n",
    "        # FFN,前馈神经网络\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(self.hidden_size, ffn_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(ffn_dim, self.hidden_size),\n",
    "        )\n",
    "        self.ffn_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "        self.ffn_dropout = nn.Dropout(dropout_rate)\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            hidden_states,\n",
    "            attn_mask=None,\n",
    "            encoder_outputs=None,\n",
    "            cross_attn_mask=None,\n",
    "    ):\n",
    "        # self-attention,自注意力\n",
    "        self_attn_outputs = self.self_attn(\n",
    "            hidden_states, hidden_states, hidden_states, attn_mask\n",
    "        )  # self_attn_outputs 是 AttentionOutput 类型\n",
    "        self_embeds = self.self_ln(\n",
    "            hidden_states + self.self_dropout(self_attn_outputs.hidden_states)\n",
    "        )  #多头注意力进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "\n",
    "        # cross-attention，交叉注意力\n",
    "        if self.cross_attn is not None:\n",
    "            assert encoder_outputs is not None, \"encoder_outputs should not be None\"\n",
    "            cross_attn_outputs = self.cross_attn(\n",
    "                self_embeds, encoder_outputs, encoder_outputs, cross_attn_mask\n",
    "            )  # query是self_embeds，key和value都是encoder_outputs\n",
    "            cross_embeds = self.cross_ln(\n",
    "                self_embeds + self.cross_dropout(cross_attn_outputs.hidden_states)\n",
    "            )  # 交叉注意力进行dropout，然后和self_embeds进行残差连接，然后进行层归一化\n",
    "\n",
    "        # FFN\n",
    "        # 如果有交叉注意力，则使用交叉注意力的输出作为FFN的输入；否则，使用self_embeds作为FFN的输入\n",
    "        embeds = cross_embeds if self.cross_attn is not None else self_embeds\n",
    "        # embeds:[batch_size, seq_len, hidden_size]\n",
    "        ffn_output = self.ffn(embeds)  # 前馈神经网络\n",
    "        # ffn_output:[batch_size, seq_len, hidden_size]\n",
    "        # 前馈神经网络进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "        embeds = self.ffn_ln(embeds + self.ffn_dropout(ffn_output))\n",
    "\n",
    "        return TransformerBlockOutput(\n",
    "            hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_outputs.attn_scores,\n",
    "            cross_attn_scores=cross_attn_outputs.attn_scores if self.cross_attn is not None else None,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19be62a364cb5cc2",
   "metadata": {},
   "source": [
    "## Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f0e09edb3e7bbfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.497908Z",
     "start_time": "2025-02-06T14:04:30.490811Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:22.065018Z",
     "iopub.status.busy": "2025-02-07T09:47:22.064560Z",
     "iopub.status.idle": "2025-02-07T09:47:22.072512Z",
     "shell.execute_reply": "2025-02-07T09:47:22.071731Z",
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    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class TransformerEncoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerEncoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_encoder_layers\"]\n",
    "\n",
    "        # layers,仅仅是一个模块的列表，它本身没有定义前向传递（forward pass）过程。你需要在 forward 方法中明确地定义如何使用这些模块。\n",
    "        self.layers = nn.ModuleList(\n",
    "            [TransformerBlock(config) for _ in range(self.num_layers)]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self, encoder_inputs_embeds, attn_mask=None\n",
    "    ) -> TransformerEncoderOutput:\n",
    "        # 存储每个层的注意力分数\n",
    "        attn_scores = []\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = encoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config)\n",
    "            block_outputs = layer(embeds, attn_mask=attn_mask)\n",
    "            # 在每个层的输出中，提取了隐藏状态 block_outputs.hidden_states，\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            # 并将对应的注意力分数 block_outputs.self_attn_scores 添加到列表 attn_scores 中。\n",
    "            attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的注意力分数,用于画图\n",
    "\n",
    "        return TransformerEncoderOutput(\n",
    "            last_hidden_states=embeds, attn_scores=attn_scores\n",
    "        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a142b31a53d27997",
   "metadata": {},
   "source": [
    "## Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0e7f64561cbd5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.505801Z",
     "start_time": "2025-02-06T14:04:30.498905Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:22.073785Z",
     "iopub.status.busy": "2025-02-07T09:47:22.073479Z",
     "iopub.status.idle": "2025-02-07T09:47:22.083204Z",
     "shell.execute_reply": "2025-02-07T09:47:22.082445Z",
     "shell.execute_reply.started": "2025-02-07T09:47:22.073761Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerDecoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    self_attn_scores: List[Tensor]\n",
    "    cross_attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerDecoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_decoder_layers\"]\n",
    "\n",
    "        # layers\n",
    "        self.layers = nn.ModuleList(\n",
    "            [\n",
    "                TransformerBlock(config, add_cross_attention=True)\n",
    "                for _ in range(self.num_layers)\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            decoder_inputs_embeds,\n",
    "            encoder_outputs,\n",
    "            attn_mask=None,\n",
    "            cross_attn_mask=None,\n",
    "    ) -> TransformerDecoderOutput:\n",
    "        self_attn_scores = []  # 存储每个层的自注意力分数\n",
    "        cross_attn_scores = []  # 存储每个层的交叉注意力分数\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = decoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config, add_cross_attention=True)\n",
    "            # 为什么交叉注意力的mask要传入cross_attn_mask而不是attn_mask？\n",
    "            # 因为计算MutilHeadAttention的时候,keys和values都是encoder_outputs，query是decoder的输出，因此需要传入cross_attn_mask。\n",
    "            block_outputs = layer(\n",
    "                embeds,\n",
    "                attn_mask=attn_mask,  # 自注意力的mask\n",
    "                encoder_outputs=encoder_outputs,\n",
    "                cross_attn_mask=cross_attn_mask,  # 交叉注意力的mask\n",
    "            )\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            self_attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的自注意力分数\n",
    "            cross_attn_scores.append(block_outputs.cross_attn_scores)  # 存储每个层的交叉注意力分数\n",
    "\n",
    "        return TransformerDecoderOutput(\n",
    "            last_hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_scores,\n",
    "            cross_attn_scores=cross_attn_scores,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c345d9c0e326a13",
   "metadata": {},
   "source": [
    "#### mask\n",
    "\n",
    "- mask实际上大类上只有两种\n",
    "    1. `padding_mask`：mask掉`pad_idx`，不计算损失\n",
    "    2. `attention_mask`：mask掉`pad_idx`，不计算注意力分数\n",
    "- Decoder的`attention_mask`和Encoder有一定的区别：\n",
    "    - Encoder可以同时看见序列所有信息，故只mask掉`pad_idx`\n",
    "    - Decoder只能看到在自身之前的序列的信息，故要额外mask掉自身之后的序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9baa438dbfbcdd8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.512835Z",
     "start_time": "2025-02-06T14:04:30.506798Z"
    },
    "execution": {
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     "shell.execute_reply": "2025-02-07T09:47:22.092607Z",
     "shell.execute_reply.started": "2025-02-07T09:47:22.086852Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[False, False, False, False, False],\n",
      "        [ True, False, False, False, False],\n",
      "        [ True,  True, False, False, False],\n",
      "        [ True,  True,  True, False, False],\n",
      "        [ True,  True,  True,  True, False]])\n",
      "--------------------------------------------------\n",
      "tensor([[False,  True,  True,  True,  True],\n",
      "        [False, False,  True,  True,  True],\n",
      "        [False, False, False,  True,  True],\n",
      "        [False, False, False, False,  True],\n",
      "        [False, False, False, False, False]])\n"
     ]
    }
   ],
   "source": [
    "print((torch.triu(torch.ones(5, 5)) == 0))\n",
    "print(\"-\" * 50)\n",
    "# 符合Decoder的attention_mask形式\n",
    "print((torch.triu(torch.ones(5, 5)) == 0).transpose(-1, -2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52343e686b4eed69",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.667599Z",
     "start_time": "2025-02-06T14:04:30.513830Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:22.094351Z",
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     "shell.execute_reply": "2025-02-07T09:47:22.242946Z",
     "shell.execute_reply.started": "2025-02-07T09:47:22.094330Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_square_subsequent_mask(sz: int) -> Tensor:\n",
    "    # The masked positions are filled with True.\n",
    "    # Unmasked positions are filled with False.\n",
    "    # torch.ones(sz, sz): 创建一个全为 1 的 sz × sz 的矩阵。\n",
    "    # torch.triu(...): 使用 triu 函数取得矩阵的上三角部分，将主对角线以下部分置零。\n",
    "    mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "    return mask\n",
    "\n",
    "\n",
    "#画出一个16×16的矩阵热力图，黄色部分为True，是掩码\n",
    "plt.matshow(generate_square_subsequent_mask(16))\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"keys\")\n",
    "plt.ylabel(\"querys\")\n",
    "plt.title(\"1 means mask while 0 means unmask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2548d49c0f7a8fd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.199068Z",
     "start_time": "2025-02-06T14:04:30.668599Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:22.244597Z",
     "iopub.status.busy": "2025-02-07T09:47:22.244382Z",
     "iopub.status.idle": "2025-02-07T09:47:22.739950Z",
     "shell.execute_reply": "2025-02-07T09:47:22.739097Z",
     "shell.execute_reply.started": "2025-02-07T09:47:22.244579Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[BOS]', '[UNK]', 'quick', 'brown', '[UNK]', 'jumps', 'over', 'the', '[UNK]', 'dog', '.', '[EOS]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "['[BOS]', '[UNK]', 'does', 'the', '[UNK]', 'say', '?', '[EOS]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#通过下面代码查看mask的效果\n",
    "inputs_words = [\"The quick brown fox jumps over the lazy dog .\", \"What does the fox say ?\"]\n",
    "\n",
    "inputs_ids, inputs_mask = tokenizer.encode([w.split() for w in inputs_words], return_mask=True)\n",
    "for i in range(len(inputs_ids)):\n",
    "    decode_text = \\\n",
    "        tokenizer.decode(inputs_ids[i:i + 1].tolist(), remove_bos=False, remove_eos=False, remove_pad=False,\n",
    "                         split=True)[0]\n",
    "    print(decode_text)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    print(inputs_mask[i].reshape(1, -1))\n",
    "    print(\"-\" * 50)\n",
    "    # reshape(1, -1):将 input_mask[i] 的形状调整为 (1, sequence_length)\n",
    "    # repeat_interleave 在第 0 维（dim=0）上重复 sequence_length 次，生成形状为 (sequence_length, sequence_length) 的掩码。\n",
    "    self_attn_mask = inputs_mask[i].reshape(1, -1).repeat_interleave(inputs_ids.shape[-1], dim=0)\n",
    "    print(self_attn_mask)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    look_ahead_mask = generate_square_subsequent_mask(inputs_ids.shape[-1])\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axs[0].matshow(self_attn_mask)\n",
    "    axs[0].set_title(\"self_attn_mask\")\n",
    "    axs[0].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_ylabel(\"querys\")\n",
    "    axs[0].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_xlabel(\"keys\")\n",
    "    axs[1].matshow(look_ahead_mask)\n",
    "    axs[1].set_title(\"look_ahead_mask\")\n",
    "    axs[1].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_ylabel(\"querys\")\n",
    "    axs[1].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_xlabel(\"keys\")\n",
    "    plt.show()\n",
    "    print('-' * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c5b12ee51329e8",
   "metadata": {},
   "source": [
    "## Transformer Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89a32ab3ca75bf0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.215535Z",
     "start_time": "2025-02-06T14:04:31.200067Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:22.741437Z",
     "iopub.status.busy": "2025-02-07T09:47:22.741110Z",
     "iopub.status.idle": "2025-02-07T09:47:22.770746Z",
     "shell.execute_reply": "2025-02-07T09:47:22.769789Z",
     "shell.execute_reply.started": "2025-02-07T09:47:22.741406Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerOutput:\n",
    "    logits: Tensor\n",
    "    encoder_last_hidden_states: Tensor\n",
    "    encoder_attn_scores: List[Tensor]  #画图\n",
    "    decoder_last_hidden_states: Tensor\n",
    "    decoder_self_attn_scores: List[Tensor]  #画图\n",
    "    decoder_cross_attn_scores: List[Tensor]  #画图\n",
    "    preds: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]\n",
    "        self.num_encoder_layers = config[\"num_encoder_layers\"]\n",
    "        self.num_decoder_layers = config[\"num_decoder_layers\"]\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        self.bos_idx = config[\"bos_idx\"]\n",
    "        self.eos_idx = config[\"eos_idx\"]\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "        # 是否共享词嵌入，\n",
    "        # 共享后，decoder的词嵌入层和encoder的词嵌入层相同，节省内存\n",
    "        self.share = config[\"share_embedding\"]\n",
    "\n",
    "        # layers\n",
    "        self.src_embedding = TransformerEmbedding(config)  # 输入的嵌入层\n",
    "        # 如果共享词嵌入，则使用src_embedding作为trg_embedding\n",
    "        if self.share:\n",
    "            # 源和目标的嵌入层相同，共享参数，节省内存\n",
    "            self.trg_embedding = self.src_embedding\n",
    "            self.linear = lambda x: torch.matmul(\n",
    "                # x是该层的输入，[batch_size, seq_len, hidden_size]\n",
    "                # self.trg_embedding.get_word_embedding_weight().T: [hidden_size,vocab_size]\n",
    "                x, self.trg_embedding.get_word_embedding_weight().T\n",
    "            )  # 输出层，共享参数，直接拿原有embedding矩阵的转置，节省内存\n",
    "        else:\n",
    "            self.trg_embedding = TransformerEmbedding(config)  # decoder模块的嵌入层\n",
    "            self.linear = nn.Linear(self.hidden_size, self.vocab_size)  # 输出层\n",
    "\n",
    "        self.encoder = TransformerEncoder(config)\n",
    "        self.decoder = TransformerDecoder(config)\n",
    "\n",
    "        self._init_weights()  # init weights\n",
    "\n",
    "    def _init_weights(self):\n",
    "        # self.parameters(): 这个方法返回模型中的所有可学习参数（权重和偏置）。\n",
    "        for p in self.parameters():\n",
    "            if p.dim() > 1:  # 如果参数是二维或更高维（通常是权重矩阵），则执行初始化。\n",
    "                nn.init.xavier_uniform_(p)  # 使用 xavier 均匀分布来初始化权重\n",
    "\n",
    "    def generate_square_subsequent_mask(self, sz: int) -> Tensor:\n",
    "        # 为了生成斜三角的mask\n",
    "        mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "        return mask\n",
    "\n",
    "    # 用于训练,在TransformerBlock中的每一层内是并行的，层间是串行的\n",
    "    def forward(\n",
    "            self, encoder_inputs, decoder_inputs, encoder_inputs_mask=None\n",
    "    ) -> TransformerOutput:\n",
    "        # encoder_inputs: [batch_size, src_len]\n",
    "        # decoder_inputs: [batch_size, trg_len]\n",
    "        # encoder_inputs_mask: [batch_size, src_len]\n",
    "        if encoder_inputs_mask is None:\n",
    "            # 返回一个布尔张量，形状与 encoder_inputs 相同。\n",
    "            # 该布尔张量的每个位置会根据 encoder_inputs 中的值是否等于 self.pad_idx 来设置：\n",
    "            # 如果相等，值为 True（表示该位置是填充位置）。\n",
    "            # 如果不相等，值为 False（表示该位置是有效输入） \n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        # unsqueeze(1): 这个操作在张量的第 1 个维度上增加一个新的维度\n",
    "        #    得到 (batch_size, 1, src_len)。\n",
    "        # unsqueeze(2): 这个操作在张量的第 2 个维度上再次增加一个新的维度。\n",
    "        #    得到 (batch_size, 1, 1, src_len)。\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(decoder_inputs.shape[-1])  # [trg_len, trg_len]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(decoder_inputs.device)\n",
    "        )  # [1, 1, trg_len, trg_len] 用于decoder的自注意力\n",
    "\n",
    "        # 增加decoder_inputs_mask和look_ahead_mask进行组合\n",
    "        decoder_inputs_mask = decoder_inputs.eq(self.pad_idx)\n",
    "        # [batch_size, trg_len]，和上面encoder_inputs_mask一致\n",
    "        decoder_inputs_mask = decoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, trg_len]\n",
    "        decoder_inputs_mask = decoder_inputs_mask + look_ahead_mask\n",
    "        # [batch_size, 1, 1, trg_len]与[1, 1, trg_len, trg_len]相加，得到decoder的自注意力mask\n",
    "        # decoder_inputs_mask : [batch_size, 1, trg_len,trg_len]\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        encoder_outputs = self.encoder(\n",
    "            encoder_inputs_embeds, attn_mask=encoder_inputs_mask\n",
    "        )  # encoder_inputs_mask用于encoder的自注意力,广播去做计算 \n",
    "\n",
    "        # decoding\n",
    "        # decoder_inputs_embeds是TransformerEmbedding(config)\n",
    "        decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "        # decoder是TransformerDecoder(config)\n",
    "        decoder_outputs = self.decoder(\n",
    "            decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "            encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "            attn_mask=decoder_inputs_mask,  # 用于decoder的自注意力,广播去做计算\n",
    "            cross_attn_mask=encoder_inputs_mask,  # 用于decoder的交叉注意力,广播去做计算\n",
    "        )\n",
    "        # decoder_outputs.last_hidden_states: [batch_size, trg_len, hidden_size]\n",
    "        logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "        # [batch_size, trg_len, vocab_size]\n",
    "\n",
    "        return TransformerOutput(\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n",
    "\n",
    "    @torch.no_grad()  # 禁用梯度计算\n",
    "    def infer(self, encoder_inputs, encoder_inputs_mask=None) -> TransformerOutput:\n",
    "        # 应对多个样本同时进行推理\n",
    "        if encoder_inputs_mask is None:\n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(self.max_length)\n",
    "        # [max_length, max_length]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(encoder_inputs.device)\n",
    "        )  # [1, 1, max_length, max_length] 用于decoder的自注意力\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        # 因为只支持单样本预测，没有paddings，所以不需要mask\n",
    "        encoder_outputs = self.encoder(encoder_inputs_embeds)\n",
    "        # encoder_outputs.last_hidden_states: [batch_size, src_len, hidden_size]\n",
    "\n",
    "        # decoding,多样本推理\n",
    "        # encoder_inputs.shape[0]: 获取编码器输入的批量大小（batch size），即样本的数量。\n",
    "        # .reshape(-1, 1): 这个操作将张量的形状调整为 (batch_size, 1)，\n",
    "        decoder_inputs = torch.Tensor([self.bos_idx] * encoder_inputs.shape[0]).reshape(-1, 1).long().to(\n",
    "            device=encoder_inputs.device)\n",
    "        # 推理是串行的，每次只推理一个token，所以需要初始化decoder_inputs为bos_idx\n",
    "        for cur_len in tqdm(range(1, self.max_length + 1)):\n",
    "            decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "            decoder_outputs = self.decoder(\n",
    "                decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "                encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "                # decoder的自注意力mask\n",
    "                # 因为是串行的，所以每次只看前面cur_len个token\n",
    "                attn_mask=look_ahead_mask[:, :, :cur_len, :cur_len],\n",
    "            )\n",
    "            # decoder_outputs.last_hidden_states: [batch_size, cur_len, hidden_size]\n",
    "            logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "            # logits: [batch_size, cur_len, vocab_size]\n",
    "            # 通过最大下标确定类别，[:, -1:]表示取最后一个结果\n",
    "            next_token = logits.argmax(dim=-1)[:, -1:]  # [batch_size, 1]\n",
    "            # 预测输出拼接到输入中\n",
    "            decoder_inputs = torch.cat([decoder_inputs, next_token], dim=-1)\n",
    "            # dcoder_inputs: [batch_size, cur_len+1]\n",
    "\n",
    "            # (decoder_inputs == self.eos_idx).sum(dim=-1)是判断样本中是否含有EOS标记\n",
    "            # all是每一个都为True，才会结束\n",
    "            if all((decoder_inputs == self.eos_idx).sum(dim=-1) > 0):\n",
    "                break\n",
    "\n",
    "        return TransformerOutput(\n",
    "            preds=decoder_inputs[:, 1:],  # 去掉bos_idx\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a740def7afa83f9",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18175b791877293",
   "metadata": {},
   "source": [
    "## 损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2b1b4622966eb25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.222855Z",
     "start_time": "2025-02-06T14:04:31.217548Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:22.772356Z",
     "iopub.status.busy": "2025-02-07T09:47:22.771856Z",
     "iopub.status.idle": "2025-02-07T09:47:22.779209Z",
     "shell.execute_reply": "2025-02-07T09:47:22.778409Z",
     "shell.execute_reply.started": "2025-02-07T09:47:22.772327Z"
    }
   },
   "outputs": [],
   "source": [
    "class CrossEntropyWithPadding:\n",
    "    def __init__(self, config):\n",
    "        self.label_smoothing = config[\"label_smoothing\"]\n",
    "\n",
    "    def __call__(self, logits, labels, padding_mask=None):\n",
    "        # logits: [batch_size, seq_len, vocab_size]\n",
    "        # labels: [batch_size, seq_len]\n",
    "        # padding_mask: [batch_size, seq_len]\n",
    "        bs, seq_len, nc = logits.shape\n",
    "        loss = F.cross_entropy(\n",
    "            logits.reshape(bs * seq_len, nc),\n",
    "            labels.reshape(-1),\n",
    "            reduction='none',  # 不对损失进行归约（reduction\n",
    "            label_smoothing=self.label_smoothing\n",
    "        )  # label_smoothing表示随机将一个类别的概率设置为0.1，使得模型更加关注其他类别\n",
    "        # loss: [batch_size * seq_len, 1]\n",
    "\n",
    "        if padding_mask is None:\n",
    "            loss = loss.mean()  # 计算平均损失\n",
    "        else:\n",
    "            # 将padding_mask reshape成一维张量，mask部分为0，非mask部分为1\n",
    "            padding_mask = 1 - padding_mask.reshape(-1)\n",
    "            loss = torch.mul(loss, padding_mask).sum() / padding_mask.sum()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab581173629b9cc",
   "metadata": {},
   "source": [
    "## 学习率衰减"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39efc556d490f34e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.228130Z",
     "start_time": "2025-02-06T14:04:31.223857Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:22.780639Z",
     "iopub.status.busy": "2025-02-07T09:47:22.780283Z",
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     "shell.execute_reply": "2025-02-07T09:47:22.785631Z",
     "shell.execute_reply.started": "2025-02-07T09:47:22.780610Z"
    }
   },
   "outputs": [],
   "source": [
    "# NoamDecayScheduler 是一个自定义或外部定义的学习率衰减调度器类。\n",
    "# 它需要接收配置 config 作为参数，可能实现了特定的学习率衰减方案\n",
    "class NoamDecayScheduler:\n",
    "    def __init__(self, config):\n",
    "        self.d_model = config[\"d_model\"]\n",
    "        self.warmup_steps = config[\"warmup_steps\"]\n",
    "\n",
    "    # __call__ 方法是一个特殊的方法，用于使对象能够像函数一样被调用\n",
    "    def __call__(self, step):\n",
    "        step += 1\n",
    "        arg1 = step ** (-0.5)  # 4000步之后是arg1\n",
    "        arg2 = step * (self.warmup_steps ** (-1.5))  # 4000步之前是arg2\n",
    "        arg3 = self.d_model ** (-0.5)\n",
    "\n",
    "        return arg3 * np.minimum(arg1, arg2)  # minimum是取两个数中的较小值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3188d631aaa09057",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.334627Z",
     "start_time": "2025-02-06T14:04:31.229133Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:22.787643Z",
     "iopub.status.busy": "2025-02-07T09:47:22.787368Z",
     "iopub.status.idle": "2025-02-07T09:47:22.892542Z",
     "shell.execute_reply": "2025-02-07T09:47:22.891850Z",
     "shell.execute_reply.started": "2025-02-07T09:47:22.787622Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_learning_rate_schedule = NoamDecayScheduler({\"d_model\": 512, \"warmup_steps\": 4000})\n",
    "#下面是学习率的设计图\n",
    "plt.plot(temp_learning_rate_schedule(np.arange(0, 40000)))\n",
    "plt.ylabel(\"Leraning rate\")\n",
    "plt.xlabel(\"Train step\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90185dd5b91d52ad",
   "metadata": {},
   "source": [
    "## 优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5061e4cd6464c1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.339984Z",
     "start_time": "2025-02-06T14:04:31.335629Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:22.893876Z",
     "iopub.status.busy": "2025-02-07T09:47:22.893381Z",
     "iopub.status.idle": "2025-02-07T09:47:22.898838Z",
     "shell.execute_reply": "2025-02-07T09:47:22.898325Z",
     "shell.execute_reply.started": "2025-02-07T09:47:22.893846Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import LambdaLR\n",
    "from torch.optim import Adam\n",
    "\n",
    "\n",
    "def get_optimizer(model, config):\n",
    "    base_lr = 0.1\n",
    "    beta1 = config[\"beta1\"]  # Adam 的 beta1\n",
    "    beta2 = config[\"beta2\"]  # Adam 的 beta2\n",
    "    eps = config[\"eps\"]\n",
    "    optimizer = Adam(model.parameters(), lr=base_lr, betas=(beta1, beta2), eps=eps)\n",
    "    # config是一个字典，包含了学习率衰减的参数\n",
    "    lr_scheduler = NoamDecayScheduler(config)\n",
    "    # 使用 LambdaLR 调度器，它可以根据给定的函数 lr_lambda 调整学习率。\n",
    "    # 这里将 lr_scheduler 作为函数传递给 LambdaLR，它包含了特定于模型或任务的学习率调度规则\n",
    "    scheduler = LambdaLR(optimizer, lr_lambda=lr_scheduler)\n",
    "    return optimizer, scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49a2f5433a409089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.346703Z",
     "start_time": "2025-02-06T14:04:31.340986Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:22.899774Z",
     "iopub.status.busy": "2025-02-07T09:47:22.899515Z",
     "iopub.status.idle": "2025-02-07T09:47:22.904917Z",
     "shell.execute_reply": "2025-02-07T09:47:22.904360Z",
     "shell.execute_reply.started": "2025-02-07T09:47:22.899755Z"
    }
   },
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        self.save_dir = save_dir  # 保存路径\n",
    "        self.save_step = save_step  # 保存步数\n",
    "        self.save_best_only = save_best_only  # 是否只保存最好的模型\n",
    "        self.best_metric = -np.inf  # 最好的指标，指标不可能为负数，所以初始化为-1\n",
    "        # 创建保存路径\n",
    "        if not os.path.exists(self.save_dir):  # 如果不存在保存路径，则创建\n",
    "            os.makedirs(self.save_dir)\n",
    "\n",
    "    # 对象被调用时：当你将对象像函数一样调用时，Python 会自动调用 __call__ 方法。\n",
    "    # state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "    # metric 是指标，可以是验证集的准确率，也可以是其他指标\n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return  # 不是保存步数，则直接返回\n",
    "\n",
    "        if self.save_best_only:\n",
    "            assert metric is not None  # 必须传入metric\n",
    "            if metric >= self.best_metric:  # 如果当前指标大于最好的指标\n",
    "                # save checkpoint\n",
    "                # 保存最好的模型，覆盖之前的模型，不保存step，只保存state_dict，即模型参数，不保存优化器参数\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"origin.ckpt\"))\n",
    "                self.best_metric = metric  # 更新最好的指标\n",
    "        else:\n",
    "            # 保存模型\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "            # 保存每个step的模型，不覆盖之前的模型，保存step，保存state_dict，即模型参数，不保存优化器参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c98380e43240291",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.352748Z",
     "start_time": "2025-02-06T14:04:31.347708Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:22.905739Z",
     "iopub.status.busy": "2025-02-07T09:47:22.905552Z",
     "iopub.status.idle": "2025-02-07T09:47:22.909914Z",
     "shell.execute_reply": "2025-02-07T09:47:22.909389Z",
     "shell.execute_reply.started": "2025-02-07T09:47:22.905711Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        self.patience = patience  # 多少个step没有提升就停止训练\n",
    "        self.min_delta = min_delta  # 最小的提升幅度\n",
    "        self.best_metric = -np.inf  # 记录的最好的指标\n",
    "        self.counter = 0  # 计数器，记录连续多少个step没有提升\n",
    "\n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:  # 如果指标提升了\n",
    "            self.best_metric = metric  # 更新最好的指标\n",
    "            self.counter = 0  # 计数器清零\n",
    "        else:\n",
    "            self.counter += 1  # 计数器加一\n",
    "\n",
    "    @property  # 使用@property装饰器，使得 对象.early_stop可以调用，不需要()\n",
    "    def early_stop(self):\n",
    "        # 如果计数器大于等于patience，则返回True，停止训练\n",
    "        return self.counter >= self.patience"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb3c8c230d312c1",
   "metadata": {},
   "source": [
    "## training & valuating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "140f2acfbeb9740a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.358185Z",
     "start_time": "2025-02-06T14:04:31.353752Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:22.910678Z",
     "iopub.status.busy": "2025-02-07T09:47:22.910505Z",
     "iopub.status.idle": "2025-02-07T09:47:22.914924Z",
     "shell.execute_reply": "2025-02-07T09:47:22.914402Z",
     "shell.execute_reply.started": "2025-02-07T09:47:22.910660Z"
    }
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()  # 禁用梯度计算\n",
    "def evaluating(model, data_loader, loss_fct):\n",
    "    loss_list = []\n",
    "    for batch in data_loader:\n",
    "        encoder_inputs = batch[\"encoder_inputs\"]\n",
    "        encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "        decoder_inputs = batch[\"decoder_inputs\"]\n",
    "        decoder_labels = batch[\"decoder_labels\"]\n",
    "        decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "        # 前向计算\n",
    "        outputs = model(\n",
    "            encoder_inputs=encoder_inputs,\n",
    "            decoder_inputs=decoder_inputs,\n",
    "            encoder_inputs_mask=encoder_inputs_mask,\n",
    "        )\n",
    "        logits = outputs.logits\n",
    "        # 计算损失\n",
    "        loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "        loss_list.append(loss.cpu().item())\n",
    "\n",
    "    return np.mean(loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3099e48677dcb6bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.366317Z",
     "start_time": "2025-02-06T14:04:31.359188Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:22.915764Z",
     "iopub.status.busy": "2025-02-07T09:47:22.915531Z",
     "iopub.status.idle": "2025-02-07T09:47:22.923888Z",
     "shell.execute_reply": "2025-02-07T09:47:22.923286Z",
     "shell.execute_reply.started": "2025-02-07T09:47:22.915746Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def training(model,\n",
    "             train_loader,\n",
    "             val_loader,\n",
    "             epoch,\n",
    "             loss_fct,\n",
    "             optimizer,\n",
    "             scheduler=None,\n",
    "             save_ckpt_callback=None,\n",
    "             early_stop_callback=None,\n",
    "             eval_step=500,\n",
    "             ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "\n",
    "    global_step = 1  # 全局步数\n",
    "    model.train()  # 训练模式\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # 训练\n",
    "            for batch in train_loader:\n",
    "                encoder_inputs = batch[\"encoder_inputs\"]\n",
    "                encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "                decoder_inputs = batch[\"decoder_inputs\"]\n",
    "                decoder_labels = batch[\"decoder_labels\"]\n",
    "                decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "                optimizer.zero_grad()  # 梯度清零\n",
    "\n",
    "                # 前向传播\n",
    "                outputs = model(\n",
    "                    encoder_inputs=encoder_inputs,\n",
    "                    decoder_inputs=decoder_inputs,\n",
    "                    encoder_inputs_mask=encoder_inputs_mask\n",
    "                )\n",
    "                logits = outputs.logits\n",
    "                loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                if scheduler is not None:\n",
    "                    scheduler.step()  # 更新学习率\n",
    "\n",
    "                loss = loss.cpu().item()\n",
    "\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss,\n",
    "                    \"step\": global_step\n",
    "                })\n",
    "\n",
    "                # 评估\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()  # 评估模式\n",
    "                    # 验证集损失和准确率\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss,\n",
    "                        \"step\": global_step\n",
    "                    })\n",
    "                    model.train()  # 训练模式\n",
    "\n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        # model.state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), -val_loss)\n",
    "                        # 保存最好的模型，覆盖之前的模型，保存step，保存state_dict,通过metric判断是否保存最好的模型\n",
    "\n",
    "                    # 3. 早停 early stopping\n",
    "                    if early_stop_callback is not None:\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict  # 早停，返回记录字典 record_dict\n",
    "\n",
    "                # 更新进度条和全局步数\n",
    "                pbar.update(1)  # 更新进度条\n",
    "                global_step += 1  # 全局步数加一\n",
    "            pbar.set_postfix({\"epoch\": epoch_id, \"loss\": loss, \"val_loss\": val_loss})\n",
    "\n",
    "    return record_dict  # 训练结束，返回记录字典 record_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf04ac5e18afea4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.441762Z",
     "start_time": "2025-02-06T14:04:31.367320Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:22.924698Z",
     "iopub.status.busy": "2025-02-07T09:47:22.924518Z",
     "iopub.status.idle": "2025-02-07T09:47:23.262777Z",
     "shell.execute_reply": "2025-02-07T09:47:23.262148Z",
     "shell.execute_reply.started": "2025-02-07T09:47:22.924680Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load train dataset from wmt16/.cache/de2en_train_128.npy\n",
      "load val dataset from wmt16/.cache/de2en_val_128.npy\n"
     ]
    }
   ],
   "source": [
    "#模型的超参\n",
    "config = {\n",
    "    \"bos_idx\": 1,\n",
    "    \"eos_idx\": 3,\n",
    "    \"pad_idx\": 0,\n",
    "    \"vocab_size\": len(word2idx),\n",
    "    \"max_length\": 128,\n",
    "    \"d_model\": 512, # 可调整\n",
    "    \"dim_feedforward\": 2048,  # FFN 的隐藏层大小\n",
    "    \"dropout\": 0.1, # 可调整\n",
    "    \"layer_norm_eps\": 1e-6,  # 层归一化的 epsilon, 防止除零错误\n",
    "    \"num_heads\": 8, # 可调整\n",
    "    \"num_decoder_layers\": 6, # 可调整\n",
    "    \"num_encoder_layers\": 6, # 可调整\n",
    "    \"label_smoothing\": 0.1,\n",
    "    \"beta1\": 0.9,  # Adam 的 beta1\n",
    "    \"beta2\": 0.98,  # Adam 的 beta2\n",
    "    \"eps\": 1e-9,\n",
    "    \"warmup_steps\": 4_000,\n",
    "    \"share_embedding\": False,  # 是否共享词向量\n",
    "}\n",
    "\n",
    "\n",
    "def get_dl(dataset, batch_size, shuffle=True):\n",
    "    sampler = TransformerBatchSampler(dataset, batch_size=batch_size, shuffle_batch=shuffle)\n",
    "    sample_dl = DataLoader(dataset, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "    return sample_dl\n",
    "\n",
    "\n",
    "# dataset\n",
    "train_ds = LangPairDataset(\"train\", max_length=config[\"max_length\"])\n",
    "val_ds = LangPairDataset(\"val\", max_length=config[\"max_length\"])\n",
    "\n",
    "# tokenizer\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word, max_length=config[\"max_length\"])\n",
    "\n",
    "# dataloader\n",
    "batch_size = 2048\n",
    "train_dl = get_dl(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_dl = get_dl(val_ds, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c4bd1cb45f5032ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:32.221751Z",
     "start_time": "2025-02-06T14:04:31.442764Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:23.263756Z",
     "iopub.status.busy": "2025-02-07T09:47:23.263530Z",
     "iopub.status.idle": "2025-02-07T09:47:24.125502Z",
     "shell.execute_reply": "2025-02-07T09:47:24.124783Z",
     "shell.execute_reply.started": "2025-02-07T09:47:23.263734Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型参数量: 71938239\n"
     ]
    }
   ],
   "source": [
    "#计算模型参数量\n",
    "model = TransformerModel(config)\n",
    "print(f\"模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6b87e4917b9ef89b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:53.810386Z",
     "start_time": "2025-02-06T14:04:32.224757Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T09:47:24.126546Z",
     "iopub.status.busy": "2025-02-07T09:47:24.126242Z",
     "iopub.status.idle": "2025-02-07T10:19:34.408861Z",
     "shell.execute_reply": "2025-02-07T10:19:34.408294Z",
     "shell.execute_reply.started": "2025-02-07T09:47:24.126524Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "38999it [32:08, 20.22it/s, epoch=36, loss=2.76, val_loss=3.5]                         "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 37 / global_step 39000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# model\n",
    "model = TransformerModel(config)\n",
    "model = model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer, scheduler = get_optimizer(model, config)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=500, save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=10,min_delta=0.001)\n",
    "\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_dl,\n",
    "    val_dl,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=500\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ac7e66d5df05c3",
   "metadata": {},
   "source": [
    "# 推理\n",
    "\n",
    "- 翻译项目的评估指标一般是BLEU4\n",
    "- 接下来进行翻译推理，并作出注意力的热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "305c0410623a9f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T10:19:34.410628Z",
     "iopub.status.busy": "2025-02-07T10:19:34.410158Z",
     "iopub.status.idle": "2025-02-07T10:19:34.633609Z",
     "shell.execute_reply": "2025-02-07T10:19:34.632986Z",
     "shell.execute_reply.started": "2025-02-07T10:19:34.410605Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "# 加载模型\n",
    "state_dict = torch.load(f\"checkpoints/origin.ckpt\", map_location=\"cpu\",weights_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6f512e7c8f2db972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T10:19:34.634619Z",
     "iopub.status.busy": "2025-02-07T10:19:34.634347Z",
     "iopub.status.idle": "2025-02-07T10:19:50.547278Z",
     "shell.execute_reply": "2025-02-07T10:19:50.546798Z",
     "shell.execute_reply.started": "2025-02-07T10:19:34.634600Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load test dataset from wmt16/.cache/de2en_test_128.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "6it [00:00, 53.06it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "21it [00:00, 66.91it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 2-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "966it [00:14, 67.41it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing loss: 3.3521636086467876\n",
      "testing bleu: 0.5849977698913187\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "test_ds = LangPairDataset(\"test\", max_length=config[\"max_length\"])\n",
    "test_dl = DataLoader(\n",
    "    test_ds, batch_size=1,\n",
    "    collate_fn=partial(collate_fn, tokenizer=tokenizer)\n",
    ")\n",
    "\n",
    "model = model.to(device)\n",
    "model.eval()\n",
    "collect = {}\n",
    "loss_collect = []\n",
    "\n",
    "predictions = []\n",
    "answers = []\n",
    "\n",
    "# 初始化BLEU分数列表\n",
    "bleu_scores = []\n",
    "for idx, batch in tqdm(enumerate(test_dl)):\n",
    "    encoder_inputs = batch[\"encoder_inputs\"]\n",
    "    encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "    decoder_inputs = batch[\"decoder_inputs\"]\n",
    "    decoder_labels = batch[\"decoder_labels\"]\n",
    "\n",
    "    # 前向计算\n",
    "    outputs = model(\n",
    "        encoder_inputs=encoder_inputs,\n",
    "        decoder_inputs=decoder_inputs,\n",
    "        encoder_inputs_mask=encoder_inputs_mask\n",
    "    )\n",
    "\n",
    "    loss = loss_fct(outputs.logits, decoder_labels)  # 验证集损失\n",
    "\n",
    "    preds = outputs.logits.argmax(dim=-1)  # 预测结果，[1,seq_len]\n",
    "    # 把preds转为英文单词\n",
    "    preds = tokenizer.decode(preds.cpu().numpy())  #['预测句子']\n",
    "    #把decoder_labels转为英文单词\n",
    "    decoder_labels = tokenizer.decode(decoder_labels.cpu().numpy())  #['标签句子']\n",
    "\n",
    "    answers.append(decoder_labels)\n",
    "\n",
    "    belu = sentence_bleu([decoder_labels[0].split()], preds[0].split(), weights=(1, 0, 0, 0))\n",
    "    bleu_scores.append(belu)\n",
    "    collect[idx] = {\n",
    "        \"loss\": loss.item(),\n",
    "        \"src_inputs\": encoder_inputs,\n",
    "        \"trg_inputs\": decoder_inputs,\n",
    "        \"mask\": encoder_inputs_mask,\n",
    "        \"trg_labels\": decoder_labels,\n",
    "        \"preds\": preds\n",
    "    }\n",
    "    loss_collect.append(loss.item())\n",
    "\n",
    "# sort collect by value\n",
    "collect = sorted(collect.items(), key=lambda x: x[1][\"loss\"])\n",
    "print(f\"testing loss: {np.array(loss_collect).mean()}\")\n",
    "belu_scores = sum(bleu_scores) / len(bleu_scores)\n",
    "print(f\"testing bleu: {belu_scores}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a9ab2039d24b72a3",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:19:50.548183Z",
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     "iopub.status.idle": "2025-02-07T10:19:50.550579Z",
     "shell.execute_reply": "2025-02-07T10:19:50.550141Z",
     "shell.execute_reply.started": "2025-02-07T10:19:50.548151Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !pip install Cython  # if failed to install fastBPE, try this line\n",
    "# !pip install fastBPE -v\n",
    "# 在 Windows 系统上并没有 sys/mman.h 文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1e7c1e0999eb365d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T10:19:50.551530Z",
     "iopub.status.busy": "2025-02-07T10:19:50.551184Z",
     "iopub.status.idle": "2025-02-07T10:19:51.036119Z",
     "shell.execute_reply": "2025-02-07T10:19:51.035491Z",
     "shell.execute_reply.started": "2025-02-07T10:19:50.551510Z"
    }
   },
   "outputs": [],
   "source": [
    "import re\n",
    "from fastBPE import fastBPE\n",
    "from sacremoses import MosesDetokenizer, MosesTokenizer\n",
    "\n",
    "# `MosesTokenizer` 和 `MosesDetokenizer` 是来自 `sacremoses` 库的工具，用于自然语言处理中的分词（Tokenization）和去标记化（Detokenization）。\n",
    "# 这些工具主要用于对文本进行预处理和后处理，通常在处理自然语言处理任务时会用到。\n",
    "\n",
    "# 这些工具通常在文本预处理和后处理过程中使用，对输入的文本进行标记化和去标记化，是一种常用的处理方式。\n",
    "# 在自然语言处理任务中，对文本进行正确的分词和还原是很重要的，而 `MosesTokenizer` 和 `MosesDetokenizer` 提供了方便、高效的工具来处理这些任务。\n",
    "\n",
    "class Translator:\n",
    "    def __init__(self, model, src_tokenizer, trg_tokenizer):\n",
    "        self.bpe = fastBPE(\"./wmt16/bpe.20000\", \"./wmt16/vocab\")\n",
    "        self.mose_tokenizer = MosesTokenizer(lang=\"de\")\n",
    "        self.mose_detokenizer = MosesDetokenizer(lang=\"en\")\n",
    "        self.model = model\n",
    "        self.model.eval()\n",
    "        self.src_tokenizer = src_tokenizer\n",
    "        self.trg_tokenizer = trg_tokenizer\n",
    "        # 匹配出现的 @@ （后面带空格的情况）。\n",
    "        # 或者匹配以 @@ 结尾的情况（可选空格）。\n",
    "        self.pattern = re.compile(r'(@@ )|(@@ ?$)')\n",
    "        \n",
    "    def draw_attention_map(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "        attn_scores (numpy.ndarray): 表示自注意力机制（self-attention）分数。\n",
    "        cross_attn_scores (numpy.ndarray): 表示交叉注意力机制的注意力分数。\n",
    "        src_words_list (list): 源语言句子的单词列表。\n",
    "        trg_words_list (list): 目标语言句子的单词列表。\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "\n",
    "        fig = plt.figure(figsize=(10, 5), constrained_layout=True) # constrained_layout=True 自动调整子图参数，使之填充整个图像区域\n",
    "        grid = plt.GridSpec(trg_len, trg_len + src_len, wspace=0.1, hspace=0.1)# wspace,hspace 控制子图之间的间距\n",
    "        #下面是attn_scores的热力图\n",
    "        self_map = fig.add_subplot(grid[:,:trg_len]) #  添加子图\n",
    "        self_map.matshow(attn_scores.mean(dim=0), cmap='viridis') # 绘制热力图，cmap表示颜色,dim=0表示对第0维求均值\n",
    "        self_map.set_yticks(range(trg_len), trg_words_list, fontsize=10)\n",
    "        self_map.set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "        #下面是cross_attn_scores的热力图\n",
    "        cross_map = fig.add_subplot(grid[:, trg_len:])\n",
    "        cross_map.matshow(cross_attn_scores.mean(dim=0), cmap='viridis')\n",
    "        cross_map.set_yticks(range(trg_len), [], fontsize=6)\n",
    "        cross_map.set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def draw_attention_maps(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list, heads_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "\n",
    "        Args:\n",
    "            - scores (numpy.ndarray): shape = [source sequence length, target sequence length]\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "        # cross_attn_scores = cross_attn_scores[:, :len(src_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "        fig, axes = plt.subplots(2, len(heads_list), figsize=(5 * len(heads_list), 10))\n",
    "        for i, heads_idx in enumerate(heads_list):\n",
    "            axes[0, i].matshow(attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[0, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[0, i].set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "            axes[0, i].set_title(f\"head {heads_idx}\")\n",
    "            axes[1, i].matshow(cross_attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[1, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[1, i].set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "            axes[1, i].set_title(f\"head {heads_idx}\")\n",
    "\n",
    "        plt.show()\n",
    "    \n",
    "    def __call__(self, sentence_list,heads_list=None,layer_idx=-1):\n",
    "        # 将输入句子列表转换为小写，并使用 MosesTokenizer 进行分词处理。\n",
    "        sentence_list = [\" \".join(self.mose_tokenizer.tokenize(s.lower())) for s in sentence_list]\n",
    "        # 将分词后的结果进行 BPE 编码，得到 tokens_list。\n",
    "        tokens_list = [s.split() for s in self.bpe.apply(sentence_list)]\n",
    "        \n",
    "        # 使用 src_tokenizer 对 tokens_list 进行编码，同时添加起始标记 ([BOS]) 和结束标记 ([EOS])。\n",
    "        encoder_input, attn_mask = self.src_tokenizer.encode(\n",
    "            tokens_list,\n",
    "            add_bos=True,\n",
    "            add_eos=True,\n",
    "            return_mask=True,\n",
    "            )\n",
    "        encoder_input = torch.Tensor(encoder_input).to(dtype=torch.int64)\n",
    "        \n",
    "        # 使用模型的 infer 方法对编码器输入进行推理，得到输出结果 outputs\n",
    "        outputs = model.infer(encoder_inputs=encoder_input, encoder_inputs_mask=attn_mask)\n",
    "        preds = outputs.preds.numpy() # 推理结果\n",
    "        \n",
    "        # 使用目标语言的 trg_tokenizer 对预测序列进行解码，得到解码后的目标语言句子列表 trg_decoded。\n",
    "        trg_decoded = self.trg_tokenizer.decode(preds, split=True, remove_eos=False, remove_bos=False, remove_pad=False)\n",
    "        # 使用源语言的 src_tokenizer 对编码器输入进行解码，得到解码后的源语言句子列表 src_decoded。为下面绘制热力图做准备。\n",
    "        src_decoded = self.src_tokenizer.decode(\n",
    "            encoder_input.numpy(),\n",
    "            split=True,\n",
    "            remove_bos=False,\n",
    "            remove_eos=False\n",
    "            )\n",
    "        \n",
    "         # draw the attention map of the last decoder block\n",
    "        for attn_score, cross_attn_score, src, trg in zip(\n",
    "            outputs.decoder_self_attn_scores[layer_idx], outputs.decoder_cross_attn_scores[layer_idx], src_decoded, trg_decoded):\n",
    "            if heads_list is None:# 如果没有指定heads_list，就画单个热力图\n",
    "                self.draw_attention_map(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                )\n",
    "            else:# 如果指定了heads_list，就画多个热力图\n",
    "                self.draw_attention_maps(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                    heads_list=heads_list,\n",
    "                    )\n",
    "        \n",
    "        #将解码后的目标语言句子列表返回，并使用 mose_detokenizer 进行去标记化，最终得到翻译后的结果。\n",
    "        return [self.mose_detokenizer.tokenize(self.pattern.sub(\"\", s).split()) for s in self.trg_tokenizer.decode(preds)] \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c5d288cf83d67b62",
   "metadata": {
    "ExecutionIndicator": {
     "show": false
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:24:16.697508Z",
     "iopub.status.busy": "2025-02-07T10:24:16.696987Z",
     "iopub.status.idle": "2025-02-07T10:24:17.859161Z",
     "shell.execute_reply": "2025-02-07T10:24:17.858472Z",
     "shell.execute_reply.started": "2025-02-07T10:24:16.697475Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading vocabulary from ./wmt16/vocab ...\n",
      "Read 762259 words (18107 unique) from vocabulary file.\n",
      "Loading codes from ./wmt16/bpe.20000 ...\n",
      "Read 20001 codes from the codes file.\n",
      "  7%|▋         | 9/128 [00:00<00:01, 73.69it/s]\n",
      "/usr/local/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the \"figure\" keyword\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['a man and a woman are eating food.']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_list = [\n",
    "    # \"Mann in einem kleinen weißen Boot auf einem See.\",  # Man in a small white boat on a lake. ans:['man in a white boat on a boat.']\n",
    "    # \"Ein Mann mit einem Eimer und ein Mädchen mit einem Hut am Strand.\", # A man with a bucket and a girl in a hat on the beach. ans:['a man with a girl and a girl on the beach with a fishing pole.']\n",
    "    # \"Drei Männer auf Pferden während eines Rennens.\",  # Three men on horses during a race. ans:['three men on horses are running in a race.']\n",
    "    \"Ein Mann und eine Frau essen zu Abend\",  # 一个男人和一个女人在吃晚餐 # ['a man and a woman are eating food.']\n",
    "]\n",
    "\n",
    "# load checkpoints\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "translator = Translator(model.cpu(), tokenizer, tokenizer)\n",
    "translator(\n",
    "    sentence_list,\n",
    "    layer_idx=-1,\n",
    "    # heads_list=[0, 1, 2, 3, 4, 5, 6, 7]\n",
    "    )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
